人工智能在金融保险风险管理中的应用

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.

This study explores the adoption and impact of artificial intelligence (AI) and machine learning (ML) in financial markets, utilizing a mixed-methods approach that includes a quantitative survey and a qualitative analysis of existing research papers, reports, and articles. The quantitative results demonstrate the growing adoption of AI and ML technologies in financial institutions and their most common applications, such as algorithmic trading, risk management, fraud detection, credit scoring, and customer service. Additionally, the qualitative analysis identifies key themes, including AI and ML adoption trends, challenges and barriers to adoption, the role of regulation, workforce transformation, and ethical and social considerations. The study highlights the need for financial professionals to adapt their skills and for organizations to address challenges, such as data privacy concerns, regulatory compliance, and ethical considerations. The research contributes to the knowledge on AI and ML in finance, helping policymakers, regulators, and professionals understand their benefits and challenges.

Artificial intelligence (AI) helps the human being get the services from the machine where the transformation of the human thinking ability into the machine works at the back end. It is believed that AI is the intelligence that is highly associated with human thinking capabilities in decision making and complex problem solving with an ability to reason, think, and improve like a human. Like other industries, AI also has the prospect in financial industries, including Islamic Financial Institutions (IFIs), in ensuring cost reduction alongside the improvement of the service quality with the efficient utilization of the resources. As the financial services sector undergoes groundbreaking journeys utilizing emerging technologies such as AI, IFIs need to be part of this stream. IFIs should build an AI strategy and concentrate on some AI technologies such as machine learning and some other popular technologies on their existing system to get under way. Many conventional banks are using AI on a large scale, so IFIs can take help from those firms. This would encourage IFIs to provide a personalized touch and improve customer service. To this end, this chapter develops a brief introduction to AI in connection with the financial industry and highlights the existing AI technologies used in both conventional financial institutions and IFIs. In doing so, this chapter identifies some of the prospects of using AI in IFIs and some challenges related to the implementation of AI in IFIs.

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.

In recent years, the emergence of big data and artificial intelligence technology has made Internet finance a brand new development model in the new era. As an emerging financial format, Internet finance plays an important role in providing people with convenient and efficient services. However, due to the late start in this regard and the imperfect related policies and regulations, China is currently still in the development stage, resulting in its risk management system not being mature and complete and lacking uniformity. There are also many regulatory deficiencies, which are not conducive to the healthy, stable, and continuous growth and progress of Internet finance. In the new situation, it is of great significance to strengthen the research on the security of China's Internet finance. Therefore, how to effectively manage Internet financial risks in the context of big data and artificial intelligence has become a topic of research. This study uses questionnaire analysis and data analysis to understand the distribution of risks and the importance of risk response measures through questionnaire surveys. According to the survey results, in the eyes of most interviewees, the ratios of operational risk, credit risk, platform operation risk, and lack of law and reputation risk in high-risk areas are 0.15, 0.3, 0.29, 0.51, and 0.1, respectively. The risks of these first-level indicators need to be particularly important and need to be effectively avoided to manage Internet financial risks. In addition, the most important risk response measures are the construction of information security, followed by the improvement of relevant laws and regulations. In their view, only from these aspects can we effectively control risks internally and externally.

Staggeringly leveraged, glacially rising interest rates, seemingly unintended consequences of central bank actions, a rolling list of systemic bank failures, an imploding crypto ecosystem, all warranting a newfound respect for all things risk. Meanwhile, AI's rampant success in other industries and banks' hitherto slow embrace of both FinTech and AI-based solutions appear tragically at odds. First, a detailed look at how banks leveraged AI to augment risk control-a rapidly rewritten regulation cast in stone, which led to the founding of the BCBS and the international standardization of risk management-a focus on huge credit risks and a general decline of market risks. Some banks went completely broke and led case-by-notable-case changes to regulation and practice. A second, stylized, glance with a broader scope ranging from LiDAR for portfolio management to support for decision-making on capital and orbiting instead of honed in on a fully different race imbued by open science, by some un{{N}} ovated datathons. Finally, with knowledge assembled from peers and failed portrayed examples of plausible new unresolved coding errors for regulators' analyses, Supervisory AI or SA on a par sphere aimed entirely at outside insurmountable errors while organ assumed by some design (Un)Financially Safe Pillars-besides T-model predictionModel/Atomic/CBM, adlength/entailed/ID-ed and refund to avoid per fall.

This study examines the application of artificial intelligence (AI) in enhancing risk management within financial services. Through comprehensive analysis, the research reveals that AI technologies, particularly machine learning, and deep learning models, significantly improve the accuracy and efficiency of risk assessment and management processes. AI-powered credit risk models demonstrate a 20% increase in predictive accuracy compared to traditional methods, while market risk management sees a 30% improvement in anomaly detection speed and precision. The study also highlights a 60% reduction in false positives for fraud detection and a 40% increase in accurate favorable rates. Despite these advancements, challenges persist, primarily in data quality and model interpretability. The research projects that by 2028, AI will be integral to risk management in over 80% of large financial institutions, potentially reducing risk-related losses by 25% and improving operational efficiency by 35%. The study concludes by emphasizing the need for strategic implementation and responsible AI use, outlining future research directions, including the long-term impact on systemic risk, ethical implications, and the potential of quantum machine learning in risk modeling. Keywords: Artificial Intelligence, Financial Risk Management, Machine Learning, Regulatory Compliance.

Abstract Artificial intelligence is widely used in people’s lives. With the popularity of artificial intelligence, the financial industry has also ushered in reforms. With other technical support, the integration of artificial intelligence technology has formed a new model of financial industry development, that is,“ Internet + big data + artificial intelligence + financial risk control”. It has gradually become popular in this rapidly developing society. And this new model will be the main form of financial risk control in the future. This paper mainly sorts out the application status of artificial intelligence in the financial field, and it analyzes its advantages and problems.

This research examines the use of artificial intelligence (AI) as a financial risk management tool. The concept is motivated by the revolutionary effects that financial technology has on business operations. Traditional methods of financial risk management are no longer effective and require revision. The purpose of the study is to assess the role of artificial intelligence in the management of financial risks and offer recommendations for its further use in the financial sector of the economy. Methodological analysis of relevant scientific literature showed that AI, in particular machine learning, can help in managing financial risks. It has been concluded that AI improves the management of market and credit risks in model verification, risk modelling, stress testing and data preparation. AI helps to monitor the quality of information received, detect fraud and search for the right information on the Internet. In the future, financial technology will continue to influence the financial sector as operating companies modify their operations. Thus, financial risk management tools will include AI. The study examines the possibilities of AI use in financial (market and credit), risk management and operational sectors (business continuity and emergency recovery). The paper presents the most promising AI technologies and techniques such as RPA, Data Management, Blockchain, MRL, MRC, CRU, Deep Learning, OML, Modelling and Stress Testing, Machine Learning and Algorithms, Neural Networks, Decision Trees, CPM, CRA, Black Box, etc. to improve “Financial Risk Management (FRM)”.

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To better promote the healthy and long-term development of corporate financial management, the basement is established on the perspective of artificial intelligence (AI). Initially, based on the theories of modern mobile payment (MP) and corporate financial leverage, the corresponding data set is obtained through the questionnaire method as the research data. The reliability coefficients obtained after the test are all above 0.65, indicating that the reliability and stability of the entire data are relatively good. Besides, it is also found from the data of the questionnaire that some residents believe that MP will bring harm such as information leakage. Next, a new multilevel evaluation analysis method is introduced. After evaluating the financial management risk, operation risk, and network security risk existing in enterprise MP, it is found that the financial management risk accounts for the largest proportion of the three, with a risk weight of about 0.54, and the capital risk occupies the main position in the financial management risk. Finally, through the analysis of the risks existing in the whole operation process of the enterprise, it is found that about 50% of the financial management risk of the enterprise in the market belongs to the advanced risk, about 30% of the operational operation risk belongs to the low risk, and about 20% of the network security risk belongs to the advanced risk, which indicates that the financial management risk and network security risk are the top priority of the enterprise MP risk. Although the operational operation risk belongs to the low risk, it cannot be ignored. Subsequently, feasible suggestions and opinions are put forward on these phenomena from the perspectives of the government, enterprises, and residents. Therefore, there is great reference significance for the current financial risk assessment of enterprises based on MP.

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AI in fraud detection and financial risk management has taken this role of prevention and combating fraud closely related to organizations and the losses they incur a next level. This paper aims to discuss the use of artificial intelligence models in the process of detecting frauds and preventing and reducing financial risks in such markets as banking, insurance, and fintech. Today, through machine learning algorithms, deep learning techniques, and data analysis, the AI improves the speed, accuracy and effectiveness of fraud detection. This paper discusses the current AI models and business use incorporating the success story and the business outcomes which has encountered sometime to have the best result. Furthermore, the paper examines other important issues of AI application management such as data security and liberation, and complete fairness control. Using examples as well as statistical data in this AI for business article, we show how corporations have managed to minimize their risks while lowering their expenses with the use of artificial intelligence technology. This research outlines ideas on how organizations can implement AI into fraud detection systems and what can be done in future to enhance the solutions. This paper adds to the emerging body of knowledge on AI’s impact on finance and security, and demonstrates AI’s ability to influence the future of the industry.

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Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come.

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The rapid advancement of Artificial Intelligence (AI) has transformed financial markets by enhancing risk management, optimizing portfolio allocation, and revolutionizing algorithmic trading.Traditional financial decision-making, which relied on historical data, expert intuition, and rule-based models, often struggled to adapt to market volatility and systemic risks.AI-driven models, powered by machine learning, deep learning, and predictive analytics, now enable real-time risk assessment, improved asset allocation strategies, and enhanced trading efficiency.By processing vast amounts of structured and unstructured data, AI systems can identify complex patterns, detect anomalies, and predict market trends with greater accuracy than conventional methods.In risk management, AI enhances predictive accuracy by analyzing macroeconomic indicators, financial statements, and alternative data sources to assess credit and market risks dynamically.AI-based portfolio optimization algorithms utilize techniques such as reinforcement learning and evolutionary computation to create adaptive investment strategies that respond to market fluctuations.Furthermore, AI has redefined algorithmic trading by enabling high-frequency trading, sentiment analysis-based strategies, and automated order execution, increasing market efficiency while minimizing latency.Despite these advancements, AI adoption in financial markets presents challenges, including model interpretability, regulatory compliance, and algorithmic bias.The black-box nature of AI models raises concerns regarding transparency and accountability, necessitating the development of Explainable AI (XAI) frameworks.Additionally, regulatory bodies are working to establish guidelines to ensure fairness, mitigate systemic risks, and promote ethical AI deployment.This paper explores AI's transformative role in financial markets, highlighting its benefits, challenges, and future research opportunities in creating a more efficient and resilient financial ecosystem.

An artificial intelligence integrated application model of supply chain financial risk assessment is constructed. Based on the financial data and supply chain data of listed companies in China’s new energy electric vehicle industry, the supply chain financial credit risk evaluation index system is constructed. The data samples are preprocessed by PCA as the input data of the support vector machine, which effectively solves the problem of high-dimensional data in supply chain finance. By improving the inertia weight of particle swarm optimization and introducing mutation operation, a dynamic mutation particle swarm optimization algorithm is proposed to avoid the problem of particles falling into a local minimum in the process of optimization. Finally, the improved optimization algorithm is used to optimize the parameters of SVM and input AdaBoost integration as a weak classifier to build an integrated model with good performance in many aspects. The model has been successfully applied to the credit risk assessment of China’s new energy vehicle supply chain finance. The comparison with other models shows that the constructed model has certain advantages in performance.

The field of financial risk management is undergoing a significant transformation due to the advancements in artificial intelligence (AI) and the underlying machine learning (ML) techniques that provide the foundation of AI. These developments hold the potential to revolutionize the way the user’s approach and address financial risk. The expansion of AI-driven solutions has opened up various opportunities for comprehending and managing risk. These opportunities encompass a wide range of activities, such as determining appropriate lending amounts for customers in banking, issuing warning signals to financial market traders regarding position risk, identifying instances of customer and insider fraud, enhancing compliance efforts, and mitigating model risk. The prime objective of this study is to investigate the application of AI and ML in the Financial Services industry, with a specific focus on Risk Management and Fraud Detection. This study proposes an intelligent and distributed approach for detecting Internet financial fraud using Big Data. The methodology entails the utilization of the graph embedding algorithm Node2Vec for the purpose of acquiring knowledge and representing the structural characteristics of the financial network graph in the form of compact vectors with reduced dimensions. This facilitates the intelligent and effective categorization and forecasting of data samples from a dataset of significant magnitude through the utilization of a deep neural network. Based on the study's findings, it was observed that the F1-Score test outcomes obtained from the Node2Vec algorithm range from 67.1% to 73.4%. These results surpass the outcomes achieved by the other two algorithms used for comparison. This finding demonstrates that Node2Vec has greater stability in terms of overall performance and yields superior categorization outcomes.

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Abstract Financial markets are exposed to sharp price volatility, and defaults and errors, which results in high risks for relevant stakeholders. In such markets, success depends upon the quality and quantity of information available to assist the decision‐making. Artificial intelligence (AI), in this regard, can measure or predict systemic and systematic risk in the financial markets. This research study aims to highlight how the risks can be measured and controlled with the support and integration of modern AI and machine learning mechanisms. By performing a review‐based methodology, the study first presents an explanation of the models, which is followed by proposing a new AI‐based model. The model relies on several financial system's inputs, which include portfolio data, trade data, market data, financial reports, market condition, and sector‐wise data. The systemic and systematic risk is then assessed through a number of outputs that the AI algorithm will process, in the form of an interactive dashboard. The article further tests that model and presents benefits and implications.

In the vast sea of information, the powerful ability of machine learning(ML) algorithms to identify specific objects through goal guidance makes them stand out from the many methods people use. As a result, ML algorithms are widely used in business decision making. This paper presents a research analysis of financial risk network assessment model(FRNAM)s based on artificial intelligence(AI) and ML algorithms. The main risk sources of supply chain(SC) finance and the integrated risk assessment process are briefly discussed, the risk transmission effect of SC finance is analyzed, and the application of ML algorithms in financial risk assessment(FRA) is studied through the implementation process of ML algorithms and the introduction of decision trees; finally, the risk assessment capability of the FRNAM based on AI and ML is tested with Bank M as the research object. Finally, the risk assessment capability of the FRNAM based on AI and ML was tested with Bank M. The test results verified the effectiveness of the SC FRA model studied in this paper.

The study's relevance lies in the fact that the important problem is ensuring the sustainability of the development of the Russian economy. Its financial system is influenced by many factors, among which are increased risks and market uncertainty,

The effective application of artificial intelligence (AI) models in various fields in the field of financial risk can improve the speed of data processing, deepen the degree of data analysis and reduce the labor cost, thus effectively improving the efficiency of financial risk control. The application of AI in the field of financial risk management puts forward new requirements for the system setup and operation mode of financial supervision. With the rapid growth ofcomputer and network technology, the increase of market transaction frequency, the diversification of data sources and the development and application of big data, it brings new challenges to financial risk management based on massive data. Based on this, this paper analyzes the role of AI in promoting the reform and growth ofthe financial industry, and puts forward some countermeasures for rational use of AI in the field of financial risk management.

Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise risk management system. Base on the CCER (China Center for Economic Research) database, the early warning model for enterprise financial risk management containing five indices is proposed for enterprises. Through Logistic regression analysis, the design principle of the financial risk management system based on AI (Artificial Intelligence) technology is explained. The proposed system innovatively introduces the AI integrated learning method, optimizes objective function through XGBoost (eXtreme Gradient Boosting) algorithm, and trains the model through BP (Backpropagation) NN (Neural Network). Finally, following comparative analysis, the effectiveness of the proposed method is verified.

Artificial Intelligence (AI) has become a transformative force in financial risk management, offering innovative tools to enhance predictive accuracy, efficiency, and decision-making processes. This research explores how AI technologies, such as machine learning, natural language processing, and neural networks, are revolutionizing traditional approaches to risk assessment and mitigation. By analyzing vast datasets, AI enables financial institutions to identify potential risks, detect anomalies, and respond to crises with unprecedented speed. However, integrating AI into financial systems also presents challenges, including algorithmic biases, regulatory concerns, and cybersecurity vulnerabilities. Through an interdisciplinary approach, this study examines the benefits and limitations of AI-driven risk management solutions, offering insights into their practical applications and ethical implications. The findings highlight the potential of AI to redefine risk management practices while emphasizing the need for robust frameworks to address associated risks.

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The application of information technology and various electronic communication equipment has grown rapidly. At the same time, information technologies such as the Internet and communication networks have become increasingly mature and widely used, making e‐commerce transactions simpler and the roles of enterprises in the supply chain increasingly diversified. At this stage, supply chain finance has become an important way for small‐ and medium‐sized enterprises to finance, and it is a key step in commercial trade. However, the risk control of this model is difficult to be effectively contained. How to control its financial risk to the lowest level is the research goal of this paper. This paper analyzes and calculates the supply chain financial risks of different enterprises through a questionnaire method, a case analysis method, and a comparison method and obtains relevant data. The data results show that the entropy value of the net interest rate is 0.97, which indicates that it has a larger market share and less risk. Through wireless multimedia communication technology and artificial intelligence algorithms, the system calculation of supply chain financial risk management is much simpler. In this regard, the research proposes a scientific system for building supply chain financial risk management.

The former approaches for financial risk mitigation are warranted to be revamped, as they are no longer effective. Nevertheless, the continuous advancement in fintech has developed artificial intelligence (AI), whose powered techniques are considered to be the most effective to identify and mitigate financial risk. Visibly, the financial sector as a whole is drastically altered by artificial intelligence, which gives rise to several procedures to mitigate probable financial risks. In this context, this chapter presents the AI-based financial risk detection process, which involves the main steps used to detect financial risk and then classify its types. Likewise, the established ongoing artificial intelligence-based financial risk mitigation process contains several steps that are used to lessen potential risk. Also, the strategies used by artificial intelligence to mitigate dissimilar sorts of financial risks are discussed in great detail in this chapter. Overall, this chapter discusses how quickly this modern technology provides benefits in terms of mitigating financial risks. As well, by adopting developed artificial intelligence-based financial risk identification and mitigation procedures, financial institutions can accurately evaluate massive information and identify financial risk factors, thus laying a more scientific, accurate, and comprehensive decision-making foundation for financial risk mitigation and management.

In recent years, the economy in China has been steadily improving. The financial situation of enterprises in mainstream industries has become the focus of public concern. However, financial statement frauds, which occur frequently, greatly disrupt the economic order in the country. Thus, it is of practical significance to accurately identify and evaluate the audit risks of financial statements. For this purpose, this paper proposes an audit risk evaluation model of financial statement based on artificial neural networks (ANN). Firstly, the authors designed the audit risk indices and quantified the fraud factors of financial statement. Next, an audit risk verification model was established for financial statement and used to verify the predictions on three aspects of financial statement, namely, audit violation penalty (AVP), audit violation announcement (AVA), and financial statement restatement (FSR). Finally, a feedforward neural network was constructed based on the homomorphic encryption algorithm, which was subsequently used to evaluate and predict the audit risks of financial statements. The effectiveness of our model was proved valid through experiments.

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long‐distance wireless connections, as well as infrared technology and radio frequency technology optimized for short‐distance wireless connections. These events have a great impact on many small‐ and medium‐sized listed companies, resulting to many small‐ and medium‐sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small‐ and medium‐sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small‐ and medium‐sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small‐ and medium‐sized listed companies as the research object, this paper chooses the small‐ and medium‐sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small‐ and medium‐sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM‐SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM‐SVM is over 86%. It can be seen that in predicting the financial crisis of small‐ and medium‐sized listed companies, the FCM‐SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

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Healthcare fraud in the United States results in billions of dollars in financial losses annually, necessitating advanced technological solutions for fraud detection and risk management. Machine learning (ML) has emerged as a powerful tool in identifying fraudulent claims, mitigating risks, and enhancing financial security in healthcare billing and insurance (Anderson & Kim, 2023). This study examines the application of supervised and unsupervised ML techniques, such as decision trees, neural networks, and anomaly detection models, to detect fraudulent patterns in insurance claims (Wang et al., 2022). By analyzing large-scale electronic health records (EHRs) and claims datasets, ML algorithms can identify suspicious activities and reduce false positives, improving fraud detection accuracy (Garcia & Lee, 2023). Additionally, predictive analytics aids in risk assessment, enabling insurers and healthcare providers to proactively manage financial fraud risks (Brown et al., 2023). Despite its advantages, ML-based fraud detection systems face challenges, including data privacy concerns, interpretability issues, and regulatory compliance (Nguyen & Patel, 2023). This research highlights the effectiveness of AI-driven fraud detection models in minimizing financial losses and enhancing operational efficiency in the U.S. healthcare sector, with future implications for explainable AI and privacy-preserving ML solutions.

Abstract Insurance risk is one of the important risks faced by the insurance industry. Effective management of insurance risk is of great significance for preventing systematic risks in the insurance industry and stabilizing the stability of Chinese financial market. Risk management of insurance companies is a technical method to identify, measure and control risks. Its purpose is to directly and effectively promote the realization of organizational goals. Machine learning has a good ability to deal with non-linear classification problems. The risk assessment model based on machine learning can effectively improve the accuracy and applicability of risk assessment. The paper mainly discusses the important role and significance of big data and machine learning as an emerging data analysis method for insurance risk management, and introduces the random forest algorithm and its application in underwriting risk management.

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Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. Our comprehensive analysis reveals that while not every re-sampling technique improves model performance, all feature selection methods markedly bolster predictive accuracy. Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. This study not only advances the theoretical framework for combating insurance fraud through AI but also provides a practical blueprint for insurance companies aiming to incorporate advanced AI strategies into their fraud detection arsenals, thereby mitigating financial risks and fostering trust systems.

Abstract Sustainable development constitutes a global challenge today, and the sustainable development goals (Agenda 2030) will probably set the course for the coming decades. This paper discusses sustainability in insurance companies by combining two aspects: a social approach (the environmental impact) and a business approach (the prediction of claims due to climate change). Our objective is to analyse the impact of physical risk in a home insurance portfolio and to measure in economic terms the effect of climate change in the future, by applying machine learning methodologies. Two data sources are used: a Spanish insurance portfolio with 31,998 policies and claims from 2017 to 2022, and daily meteorological variables from 290 Spanish weather stations from 2000 to 2022. Two climate scenarios are considered: RCP 4.5 (medium impact) and RCP 8.5 (high impact). On average for the period 2023–2052, the results reveal that claims will increase by 105% for the 4.5 scenario and by 129% for the 8.5 scenario. Our paper makes a clear contribution to sustainability by analysing climate risks and their impact on an insurance portfolio. It shows the grave consequences of climate change for the insurance sector's solvency and the political implications for the financial system in general.

Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions. However, index insurance requires climate data with long historical records, global geographical coverage and fine spatial resolution at the same time, which is nearly impossible to satisfy, especially with open-access data. In this paper, we spatially downscaled gridded climate data (precipitation, temperature, and soil moisture) in coarse spatial resolution with globally available long-term historical records to finer spatial resolution, using satellite-based data and machine learning algorithms. We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield. This study employed county-level spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia. The results showed that in the majority of cases (70%), hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach. These improvements are statistically significant p≤0.05. Among other climate data, more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data. Overall, this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.

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Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

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The Usage-Based Insurance paradigm, which is receiving a lot of attention in recent years, envisages computing the car policy premium based on accident risk probability, evaluated observing the past driving history and habits. However, Usage-Based Insurance strategies are usually based on simple empirical decision rules built on travelled distance. The development of intelligent systems for smart risk prediction using the stored overall driving behaviour, without the need of other insurance or socio-demographic information, is still an open challenge. This work aims at exploring a comprehensive machine learning-based approach solely based on driving-related data of private vehicles. The anonymized dataset employed in this study is provided by the telematics company UnipolTech, and contains space/time densely measured data related to trips of almost 100000 vehicles uniformly spread on the Italian territory, recorded every 2 km by on-board telematics fix devices (black boxes), from February 2018 to February 2020. An innovative feature engineering process is proposed, with the aim of uncovering novel informative quantities able to disclose complex aspects of driving behaviour. Recent and powerful learning techniques are explored to develop advanced predictive models, able to provide a reliable accident probability for each vehicle, automatically managing the critical imbalance intrinsically peculiar this kind of datasets.

Health insurance is a useful service that can help its users gain lifesaving medical aid when they are in need. However, health insurance is also exploitable to insurance fraud through the falsification of information to increase the amount of reimbursement and cause massive loss of funds to the insurance provider. We propose the usage of machine learning to accurately determine potential health insurance fraud. The objective of conducting this research is to determine which features are the most important to determine healthcare insurance fraud. This research used a dataset provided in Kaggle titled Healthcare Provider Fraud Detection Analysis using Random Forest Classifier and Logistic Regression. The best-performing model in this test, the Logistic Regression, is then used to which features are the most important for the classification. Our research shows that the most important feature in detecting health insurance fraud is the amount of money reimbursed associated with a provider. The Logistic Regression model achieved an accuracy of 0.90, precision of 0.93, recall of 0.91, and an F1 Score of 0.90, outperforming the Random Forest model in comparative analysis.

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Introduction/Main Objectives: This study explores the application of machine-learning techniques to risk-based premium calculations for insurance guarantee schemes within the Indonesian insurance market. This study aims to develop a risk-based premium calculation model using machine-learning techniques in the Indonesian context. Background Problems: A gap exists in determining risk-based premiums for both the life and non-life insurance sectors within the Indonesian insurance market. Identifying and understanding the key variables that significantly influence risk-based capital (RBC) is important, and this research addresses this need. Novelty: This paper is the first to apply machine learning to calculate risk-based premiums in the context of the Indonesian insurance market. The distinction between the life and non-life insurance sectors in terms of the importance of its variables and itsselection of an optimal model further enrich its unique approach. Research Methods: We employed gradient-boosted and decision-tree models to identify key factors impacting risk-based capital. Furthermore, we leveraged clustering techniques to categorize companies into distinct risk tiers, aiming to enable more precise risk-based premium rate calculations. Finding/Results: The findings reveal significant differences between the life and non-life insurance sectors in terms of key variables that impact their risk-based capital. These insights lead to the categorization of insurance companies into distinct risk tiers whichhelps to more accurately calculate risk-based premiums. Conclusion: Machine learning can serve as a powerful tool in refining insurance risk management practices, ultimately benefiting insurers, policyholders, and regulators alike.

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Evaluation of risk is a key component to categorize the customers of the life insurance businesses. The underwriting technique is carried out by the industries to charge the policies appropriately. Due to the availability of data hugely, the automation of underwriting process can be done using data analytics technology. Due to this, the underwriting process becomes faster and therefore quickly processes a large number of applications. This study is carried to enhance risk assessment of the applicants of life insurance industries using predictive analytics. In this research, the Geographical Information Systems (GIS) system is used to collect the data such as Air pollution, Industrial area, Covid-19 and Malaria of various geographic areas of our country, since these factors attribute to the risk of an applicant of life insurance business. Thereafter, the research is carried out using this dataset along with another dataset containing more than 50,000 entries of normal attributes of applicants of a life insurance company. Artificial Neural Network (ANN), Decision Tree (DT), and Random forest (RF) algorithms are applied on both the datasets to predict the risks of the applicants. The results showed that random forest outperformed among all the algorithms, providing the more accurate result.

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Predicting underwriting risk has become a major challenge due to the imbalanced datasets in the field. A real-world imbalanced dataset is used in this work with 12 variables in 30144 cases, where most of the cases were classified as "accepting the insurance request", while a small percentage classified as "refusing insurance". This work developed 55 machine learning (ML) models to predict whether or not to renew policies. The models were developed using the original dataset and four data-level approaches resampling techniques: random oversampling, SMOTE, random undersampling, and hybrid methods with 11 ML algorithms to address the issue of imbalanced data (11 ML× (4 resampling techniques + unbalanced datasets) = 55 ML models). Seven classifier efficiency measures were used to evaluate these 55 models that were developed using 11 ML algorithms: logistic regression (LR), random forest (RF), artificial neural network (ANN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB), decision tree (DT), XGBoost, k-nearest neighbors (KNN), stochastic gradient boosting (SGB), and AdaBoost. The seven classifier efficiency measures namely are accuracy, sensitivity, specificity, AUC, precision, F1-measure, and kappa. CRISP-DM methodology is utilisied to ensure that studies are conducted in a rigorous and systematic manner. Additionally, RapidMiner software was used to apply the algorithms and analyze the data, which highlighted the potential of ML to improve the accuracy of risk assessment in insurance underwriting. The results showed that all ML classifiers became more effective when using resampling strategies; where Hybrid resampling methods improved the performance of machine learning models on imbalanced data with an accuracy of 0.9967 and kappa statistics of 0.992 for the RF classifier.

A universal healthcare policy success is impossible without the use of insurance instruments. The healthcare and insurance industries are on the verge of integrating seamlessly with the help of sensors and algorithms. This research work focuses on validating an algorithm that can help to model and classify health insurance risk data. Six algorithms Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) were evaluated and objective validation of these algorithms has been demonstrated. To maintain the replicability of the study the data and code are available in public repository. From the study, it is clear that the KNN algorithm is best suited as a risk classifier. This is evidence from the values of R2, error metrics, completeness score, explained variance, normalized mutual score v measure score, precision, recall, f1 score, and accuracy metrics. Secondly, the algorithms have been validated using 10 k-fold method using five types of performance metrics. In almost all cases, it was found that the KNN algorithm performs consistently and is the most suitable numerically. This can be attributed that the standard deviation remains tight of performance metrics in evaluation. From all the validation test, it can be claimed that on the current dataset, the KNN algorithm with Accuracy, Homogeneity Score Explained variance and Normalized mutual score hyper-parameter configuration is the best performer.

Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7-77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.

Climate change-induced increases in the frequency and intensity of extreme weather events year after year pose a significant challenge to the profitability of the global insurance industry. Traditional risk assessment models have limitations in predicting insurance profitability due to the difficulty in coping with the nonlinearity and complexity of climate risk. To this end, this study proposes a multi-model fusion approach that combines fuzzy assessment models, entropy weighting, linear regression, and machine learning models (LightGBM & XGBoost) to assess the impact of climate risk on the profitability of the insurance industry. By analyzing cross-country empirical data from the U.S. and U.K. insurance markets, this study reveals the differences and challenges in coping with climate risk in different countries. The findings show that climate risk significantly affects the profitability of insurance companies and that machine learning models exhibit higher accuracy and reliability in risk prediction compared to traditional methods. This paper provides an empirical basis for insurers and policymakers to address the economic impacts of climate change and makes recommendations for optimizing insurance risk management.

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Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events. This article is categorized under: Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Data: Types and Structure > Massive Data

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In the dynamic FinTech industry, proactively managing risks has become vital in securing long-term viability and resilience for lending and investmentHowever activities. Nevertheless, the industry looks promising, concurrently embracing sophisticated technologies with all accompanying financial innovations. However, the high risks due to introducing innovation make the process even more problematic.

This article explores the integration of artificial intelligence into fintech risk management frameworks, examining how predictive analytics are revolutionizing risk assessment and mitigation capabilities across the financial services industry. It investigates the evolution of risk management within the rapidly changing fintech landscape, highlighting how traditional approaches prove increasingly inadequate in addressing complex challenges like real-time fraud detection, cybersecurity threats, alternative credit assessment, cryptocurrency volatility, and decentralized finance liquidity risks. The article presents a comprehensive analysis of AI-powered solutions across key risk domains, including credit risk assessment, fraud detection, and market risk modeling, demonstrating their superior performance compared to conventional methods. It further outlines a structured framework for enterprise AI implementation, addressing the critical dimensions of data infrastructure, model development, operational integration, and continuous adaptation. The article also examines significant implementation challenges related to regulatory compliance, model explainability, data quality, and talent requirements. Finally, it explores emerging trends that will shape the future of AI-driven risk management, including federated learning, quantum computing, automated risk mitigation, and ecosystem-wide risk intelligence capabilities.

The rapid evolution of financial technology (fintech) platforms has exponentially increased the volume and sophistication of financial transactions, concurrently elevating the risk and complexity of fraudulent activities. This necessitates a paradigm shift in fraud detection methodologies towards more agile, accurate, and predictive solutions. This paper presents a comprehensive study on the transformative potential of advanced Artificial Intelligence (AI) algorithms in enhancing fintech fraud detection mechanisms. By leveraging cutting-edge AI techniques including deep learning, machine learning, and natural language processing, this research aims to develop a robust fraud detection framework capable of identifying, analyzing, and preventing fraudulent transactions in real-time. Our methodology encompasses the deployment of several AI algorithms on extensive datasets comprising genuine and fraudulent financial transactions. Through a comparative analysis, we identify the most effective algorithms in terms of accuracy, efficiency, and scalability. Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities. Furthermore, the integration of natural language processing enables the extraction and analysis of unstructured data, significantly enhancing the detection capabilities. Conclusively, this paper underscores the critical role of advanced AI algorithms in revolutionizing fintech fraud detection. It highlights the superior performance of AI-based models over conventional methods, offering fintech platforms a more dynamic and predictive approach to fraud prevention. This research not only contributes to the academic discourse on financial security but also provides practical insights for fintech companies striving to safeguard their operations against fraud. Keywords: Artificial Intelligence, Fintech, Fraud Detection, Ethical Ai, Regulatory Compliance, Data Privacy, Algorithmic Bias, Predictive Analytics, Blockchain Technology, Quantum Computing, Interdisciplinary Collaboration, Innovation, Transparency, Accountability, Continuous Learning, Ethical Principles, Real-Time Processing, Financial Sector.

In the rapidly evolving FinTech landscape, cybersecurity has become a critical priority due to the increasing sophistication of cyber threats targeting financial institutions.Predictive analytics, powered by Big Data and Machine Learning [ML], offers transformative potential in Cyber Threat Intelligence [CTI] to anticipate, detect, and mitigate risks before they materialize.This article explores the integration of predictive analytics into CTI frameworks, focusing on the processing and analysis of unstructured data from diverse sources such as dark web forums, phishing campaigns, malware logs, and social media.By leveraging ML techniques such as anomaly detection, reinforcement learning, and Natural Language Processing [NLP], organizations can enhance their ability to identify threat patterns, assess risks dynamically, and respond proactively to emerging cybersecurity challenges.The discussion highlights key applications, including threat pattern recognition using historical data, real-time dynamic risk assessment with financial transaction data, and NLP to extract actionable insights from threat intelligence feeds.Cloud security solutions, enhanced by Big Data analytics, are examined for their role in safeguarding FinTech platforms against Distributed Denial of Service [DDoS] attacks and ransomware.Furthermore, the article emphasizes the importance of automating incident response mechanisms using advanced ML models to reduce response times and operational disruptions.Through a detailed analysis of technological advancements and implementation challenges, this article underscores the strategic importance of predictive analytics in fortifying cybersecurity frameworks within the FinTech sector.It provides actionable insights for stakeholders aiming to leverage emerging technologies to enhance resilience against cyber threats.

Integrating Artificial Intelligence (AI) and Machine Learning (ML) in fintech has revolutionized trading, risk management, fraud detection, and regulatory compliance. AI-driven automation enhances efficiency, while predictive analytics improves market forecasting and decision-making. Case studies demonstrate significant transformations in financial institutions, reducing operational costs and increasing accuracy. However, challenges such as data security, model interpretability, and bias remain critical concerns. This paper explores the impact of AI and ML in fintech, analyzing their benefits, limitations, and future implications for practitioners and regulators. Recommendations for improving AI transparency and regulatory adaptability are also discussed.

The increasing complexity and competitiveness of the digital finance landscape demand more agile and data-driven strategic planning frameworks. As financial service providers evolve into fully digital ecosystems, the capacity to operationalize analytics transforming raw data into real-time, actionable insights has emerged as a critical differentiator. This presents a case study on how a mid-sized digital financial platform successfully leveraged business intelligence (BI) tools to improve strategic planning and decision-making processes across product, risk, and customer management functions. This explores the design and implementation of a robust analytics infrastructure that integrated diverse data sources, including transaction logs, customer relationship management (CRM) systems, and digital engagement platforms. Through the deployment of real-time dashboards, predictive models, and cross-functional KPIs, the organization achieved enhanced visibility into loan portfolio risk, customer lifetime value (CLV), and marketing channel performance. These analytics outputs were operationalized to support executive decision-making, budget allocation, and product innovation. Key outcomes included increased forecasting accuracy, reduced default rates, and faster iteration cycles for strategic initiatives. This also discusses implementation challenges, such as ensuring data quality, managing change among end-users, and developing a sustainable governance structure. Lessons learned emphasize the importance of aligning analytics efforts with strategic priorities and fostering a culture of data-driven decision-making across departments. In addition to documenting the operational transformation, this outlines emerging opportunities in AI-powered planning, customer-centric analytics, and the integration of alternative data sources via open banking APIs. Ultimately, this case study demonstrates that business intelligence, when effectively operationalized, not only enhances planning accuracy but also builds the adaptive capacity of digital finance institutions. It concludes with strategic recommendations for fintechs and digital banks seeking to embed analytics at the core of their growth and governance models.

<abstract> <p>Accurately predicting and anticipating financial crises becomes of paramount importance in the rapidly evolving landscape of financial technology (Fintech). There is an increasing reliance on predictive modeling and advanced analytics techniques to predict possible crises and alleviate the effects of Fintech innovations reshaping traditional financial paradigms. Financial experts and academics are focusing more on financial risk prevention and control tools based on state-of-the-art technology such as machine learning (ML), big data, and neural networks (NN). Researchers aim to prioritize and identify the most informative variables for accurate prediction models by leveraging the abilities of deep learning and feature selection (FS) techniques. This combination of techniques allows the extraction of relationships and nuanced patterns from complex financial datasets, empowering predictive models to discern subtle signals indicative of potential crises. This study developed an extended osprey optimization algorithm with a Bayesian NN to predict financial crisis (EOOABNN-PFC) technique. The EOOABNN-PFC technique uses metaheuristics and the Bayesian model to predict the presence of a financial crisis. In preprocessing, the EOOABNN-PFC technique uses a min-max scalar to scale the input data into a valid format. Besides, the EOOABNN-PFC technique applies the EOOA-based feature subset selection approach to elect the optimal feature subset, and the prediction of the financial crisis is performed using the BNN classifier. Lastly, the optimal parameter selection of the BNN model is carried out using a multi-verse optimizer (MVO). The simulation process identified that the EOOABNN-PFC technique reaches superior accuracy outcomes of 95.00% and 95.87% compared with other existing approaches under the German Credit and Australian Credit datasets.</p> </abstract>

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a critical financial application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.

The rapid evolution of technology has significantly transformed the financial services sector, positioning big data and fintech as pivotal drivers of innovation. This paper explores the integration of big data analytics and fintech solutions to address critical challenges such as operational inefficiency, limited financial inclusion, and scalability issues within the financial ecosystem. Employing a qualitative research method, this study combines an extensive literature review and case study analysis from financial institutions and fintech platforms to develop a comprehensive framework. The findings reveal that the synergy be- tween big data and fintech enables transformative advancements, including predictive risk management, automated financial processes, and enhanced accessibility to underserved populations. Additionally, blockchain based finance and decentralized financial technologies demonstrate potential for scalability, transparency, and cost efficiency. However, challenges such as fragmented regulatory frameworks, data privacy concerns, and disparities in technological infrastructure persist, particularly in emerging markets. The proposed framework emphasizes scalable and sustainable approaches tailored to diverse financial contexts, ensuring longterm adaptability and alignment with global priorities like the Sustainable Development Goals (SDGs). By fostering ethical considerations, regulatory harmonization, and region specific strategies, the study contributes to academic discourse and practical advancements in responsible financial technology implementation. The conclusion highlights the critical role of big data and fintech in revolutionizing the financial services industry, driving inclusive and sustainable growth while addressing the dynamic demands of a global economy.

Financial services industry is facing a disruptive innovation phase, driven by automation and social media connectivity, changing the fundamentals of small business access to financial products and services. The rapid development of technological innovation applied to financial services, also known as financial technology or FinTech, created new products and delivery channels. The aim of the article is to analyze the financial industry competitive dynamics drift. Competitive factors are changed by the new players redefining the financial services products proposition for clients through data management and digital platforms. The digital tools have a broad impact over the traditional business models, creating a new type of access to financial services market –characterized by speed, efficiency and client oriented strategies and redefining client experience form face to face to on-line automated interactions. The trends suggest that full automation of client relationship, advanced predictive analytics and machine learning software, will create improved client experience and increasing efficiency of risk management process. Fintech key competitive advantage is built on operational excellence, integrating different business models, big data optimization analytics.

Abstract We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

The integration of artificial intelligence in fintech is revolutionizing financial services, particularly for microfinance institutions and small and medium-sized enterprises (SMEs). This paper explores the transformative impact of AI-powered innovations in credit scoring, debt recovery, and financial access. AI-driven credit scoring leverages alternative data and advanced machine learning techniques to enhance accuracy, inclusivity, and efficiency, addressing the limitations of traditional methods. In debt recovery, AI optimizes collection processes through predictive analytics, workflow automation, and conversational tools, improving operational efficiency while fostering ethical practices and customer trust. AI also plays a pivotal role in expanding financial access, enabling underserved populations to benefit from tailored digital platforms for lending, savings, and insurance. Despite its potential, AI adoption entails risks, including data privacy concerns, algorithmic bias, and the digital divide, which require careful management. The paper concludes with recommendations for policymakers, financial institutions, and tech developers to ensure AI's ethical and inclusive deployment, fostering economic resilience and equitable growth. Keywords: Artificial Intelligence (Ai), Fintech Innovations, Microfinance Institutions (Mfis), Small And Medium-Sized Enterprises (Smes), Credit Scoring, Financial Inclusion.

Big data analytics is revolutionizing the FinTech industry, offering new opportunities for real-time decision-making, personalized financial services, and improved risk management. By leveraging advanced technologies like machine learning and artificial intelligence, financial institutions can efficiently detect fraud, predict market trends, and create innovative solutions tailored to customer needs. Big data also plays a critical role in promoting financial inclusion through alternative credit scoring models, providing access to credit for underserved populations and fostering broader participation in the financial system. However, the integration of big data into FinTech is not without its challenges. Issues such as data privacy concerns, regulatory complexities, and a shortage of skilled professionals capable of handling sophisticated analytical processes pose significant barriers. Addressing these challenges requires a multi-faceted approach, including the harmonization of global regulations, the development of a skilled workforce, and investment in cutting-edge technologies like AI and cloud computing.This study explores both the opportunities and obstacles associated with big data in FinTech, emphasizing its transformative potential for improving operational efficiency and driving innovation. By shedding light on key applications and emerging trends, this research provides actionable insights for financial institutions, policymakers, and technology developers. It highlights how big data is not only reshaping the financial sector but also contributing to a more inclusive and equitable financial ecosystem.

We have seen significant changes in the global economic environment over recent years, and as the complexity of financial relationships increases, these go hand-in-hand with increased requirements for effective risk management frameworks by financial institutions. In boom/bust economic cycles, economic or political crises, and potential systemic risks in the financial markets, they help institutions navigate through various challenges surrounding market volatility, regulatory changes, and emerging risks. In this article, we will cover the basics of risk management frameworks, provide an overview of all types, and how they are successfully applied in a financial institution, while maintaining the picture of interruption and sustainable growth. The discussion starts by addressing the fundamental elements of risk management, including credit, market, and operational risks, along with newer risks like climate change, cyber, and geopolitical risks. It analyses how these tools, advanced tools like big data analytics and artificial intelligence, are integrated into each aspect of human resources management as a transformative way of predicting and mitigating risk. It also covers the key challenges such as regulatory compliance, technological integration, and organizational resistance. It also explores strategic measures to modernize the risk management framework, focusing on agility, partnerships with FinTechs, and governance improvements. Successful frameworks are exemplified in case studies that demonstrate how institutions can adapt to new realities with compliance and operational efficiency. By looking into future themes, this article predicts the implications of digital transformation, the rise of green finance, and changing regulations, to offer actionable insights to the stakeholders. The article ends with some actionable advice for financial institutions and regulators on how to develop effective, flexible, forward-looking risk management approaches.

The financial technology domain has undertaken significant strides toward more inclusive credit scoring systems by integrating alternative data sources, prompting an exploration of how we can further simplify the process of efficiently assessing creditworthiness for the younger generation who lack traditional credit histories and collateral assets. This study introduces a novel approach leveraging social media analytics and advanced machine learning techniques to assess the creditworthiness of individuals without traditional credit histories and collateral assets. Conventional credit scoring methods tend to rely heavily on central bank credit information, especially traditional collateral assets such as property or savings accounts. We leverage demographics, personality, psycholinguistics, and social network data from LinkedIn profiles to develop predictive models for a comprehensive financial reliability assessment. Our credit scoring methods propose scoring models to produce continuous credit scores and classification models to categorize potential borrowers—particularly young individuals lacking traditional credit histories or collateral assets—as either good or bad credit risks based on expert judgment thresholds. This innovative approach questions conventional financial evaluation methods and enhances access to credit for marginalized communities. The research question addressed in this study is how to develop a credit scoring mechanism using social media data. This research contributes to the advancing fintech landscape by presenting a framework that has the potential to transform credit scoring practices to adapt to modern economic activities and digital footprints.

This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), followed closely by banks (0.857), with fintech lenders trailing (0.852). In pricing analysis, banks adjust the origination rates most sharply with borrower risk (7.20 basis points per percentage-point increase in default probability) compared to fintech (4.18 bp) and non-fintech lenders (5.43 bp). Fintechs underprice 32% of high-risk loans, highlighting limited incentive alignment under GSE securitization structures. Expanding the allowable alternative data and modest risk-retention policies could enhance fintechs’ analytical effectiveness in mortgage markets.

This paper explores integrating fintech innovations and multi-cloud environments in predictive financial modeling. Fintech advancements, such as artificial intelligence, machine learning, and blockchain, drive significant improvements in financial forecasting, risk assessment, and decision-making. Meanwhile, multi-cloud architectures provide the flexibility, scalability, and resilience necessary to support these advanced fintech solutions. The paper discusses key technologies, challenges, and opportunities associated with integrating fintech in multi-cloud environments and examines future trends that could shape the financial services industry. Strategic implications for financial institutions are considered, highlighting the evolving role of fintech and multi-cloud in enhancing operational efficiency and competitiveness. By combining fintech with multi-cloud, financial institutions are better positioned to capitalize on real-time predictive analytics, ultimately transforming the future of financial services. Keywords: Fintech Innovations, Predictive Financial Modelling, Multi-Cloud Environments, Artificial Intelligence, Financial Forecasting.

The combination of Artificial Intelligence (AI) and predictive analytics can revolutionise financial risk management, especially in the banking and fintech domains. The needs of risk management, compliance are broad in the corporation and related academic papers are deep and strong on the matter the first 50 years only are discussed at a high level of these methodologies making solutions possible in identification, prediction risk mitigation for a large variety of risks credit default, fraud, liquidity crises, regulatory violations etc. The emphasis will be on affordably improving real- time analytics and adaptive algorithms to accommodate vast, disparate data streams of transaction logs, market feeds, and customer behavior. The proposed work introduce a new hybrid prediction framework combining neural network approaches with probabilistic modeling and ensemble learning for better accuracy and scalability. It also offers a glimpse into real-world legacy financial worlds and the upstart fintech players as this concept is proven empirically. These practical deployments illustrate the fact that AI systems challenge classical models in terms of accuracy, agility, risk transparency, and automated decisions. Next then discuss implementation challenges, ethical considerations, and potential paths forward for AI to be deployed within financial ecosystem.

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Financial risk management is essential for protecting both traditional institutions and the fast-evolving fintech sector. However, fintech brings new challenges, such as increased fraud and market volatility, which conventional risk management strategies are often inadequate to address. To overcome these obstacles, fintech companies are increasingly turning to Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance and automate risk assessment, fraud detection, and decision-making processes. While these technologies offer benefits like real-time risk assessment and predictive analytics, they also introduce challenges such as algorithmic bias, regulatory compliance, and data integrity issues. In this paper, we propose a fintech-specific risk management framework that employs advanced AI techniques, including neural networks and unsupervised learning, to detect anomalies and predict risks in transaction data.

In an era of accelerating digital transformation and heightened regulatory scrutiny, financial institutions and compliance-driven sectors face increasing pressure to maintain operational stability amid evolving cyber threats, system disruptions, and compliance requirements. This paper presents a comprehensive Digital Resilience Model (DRM) designed to enhance operational continuity, risk mitigation, and regulatory alignment in such critical sectors. The proposed model integrates advanced digital technologies, including artificial intelligence (AI), real-time monitoring, predictive analytics, and automation to proactively manage disruptions and ensure business continuity. The DRM framework is structured around five core pillars: proactive risk identification, adaptive response mechanisms, real-time system monitoring, regulatory compliance automation, and continuous improvement through feedback loops. By leveraging AI-driven anomaly detection and predictive analytics, organizations can identify potential system failures and cyber threats before they escalate. The model also incorporates automated incident response protocols and policy-based decision-making tools that reduce response time and human error during operational crises. Additionally, the DRM supports continuous compliance monitoring aligned with international standards such as ISO 22301, GDPR, SOX, and Basel III. It features automated reporting, audit trail generation, and regulatory alert systems to ensure transparency and preparedness for audits or inspections. The model is built to function across hybrid, cloud-native, and on-premise environments, offering flexibility and scalability. Use cases from the banking, insurance, and fintech industries demonstrate the model’s effectiveness in safeguarding critical operations and enhancing organizational resilience. Results indicate a significant reduction in downtime, faster incident resolution, improved compliance posture, and enhanced stakeholder confidence. The study concludes that adopting a robust digital resilience framework is essential for ensuring operational stability in financial and compliance-driven environments. By embedding resilience into digital infrastructure, organizations can future-proof operations, maintain trust, and meet dynamic regulatory expectations in a digital-first world.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in cloud-based Fintech platforms is transforming the financial industry, enhancing automation, security, scalability, and decision-making processes. AI-driven innovations such as predictive analytics, fraud detection, robo-advisory services, and risk assessment have significantly improved the efficiency and accuracy of financial transactions. Cloud computing further facilitates these advancements by offering on-demand infrastructure, storage, and computational power, making AI solutions more accessible and cost-effective for Fintech firms. However, despite its advantages, the adoption of AI in cloud-based Fintech platforms presents challenges related to data security, compliance, and interoperability. This paper explores the benefits, challenges, best practices, and case studies of AI and ML integration into cloud- based Fintech platforms, providing insights into how these technologies shape the future of digital finance. Keywords Artificial Intelligence, Machine Learning, Fintech, Cloud Computing, Financial Technology, Predictive Analytics, Fraud Detection, Risk Assessment, Blockchain, Cybersecurity

The increasing reliance on cloud computing in the financial technology (FinTech) sector has introduced sophisticated cyber threats that challenge traditional security frameworks. AI-driven cybersecurity has emerged as a transformative approach, leveraging machine learning, deep learning, and predictive analytics to enhance threat detection, response automation, and fraud prevention. This study explores the evolving threat landscape in FinTech and cloud security, highlighting vulnerabilities such as phishing, ransomware, API exploits, and insider threats. It examines the role of AI-powered security mechanisms, including Zero Trust Architecture (ZTA), automated Security Information and Event Management (SIEM) systems, and behavioral analytics, in mitigating cyber risks. Additionally, the research addresses challenges in AI security, such as adversarial machine learning attacks, algorithmic bias, regulatory compliance gaps, and the ethical implications of AI-driven decision-making. Future advancements, including quantum- safe AI encryption, blockchain-integrated security frameworks, and AI-driven collaborative threat intelligence, are analyzed for their potential to enhance cybersecurity resilience. The study concludes with recommendations for strengthening AI-based security strategies in FinTech and cloud environments, emphasizing the importance of transparency, regulatory alignment, and continuous innovation. By adopting AI-powered cybersecurity solutions, financial institutions and cloud providers can proactively defend against evolving cyber threats, ensuring the integrity and security of digital financial infrastructures.

Abstract Based on a data set of 91 papers and 22 industry studies, we analyse the impact of artificial intelligence on the insurance sector using Porter’s (1985) value chain and Berliner’s (1982) insurability criteria. Additionally, we present future research directions, from both the academic and practitioner points of view. The results illustrate that both cost efficiencies and new revenue streams can be realised, as the insurance business model will shift from loss compensation to loss prediction and prevention. Moreover, we identify two possible developments with respect to the insurability of risks. The first is that the application of artificial intelligence by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry’s most inherent problems, namely asymmetric information. The second development is that artificial intelligence might change the risk landscape significantly by transforming some risks from low-severity/high-frequency to high-severity/low-frequency. This requires insurance companies to rethink traditional insurance coverage and design adequate insurance products.

This paper examines the promises and pitfalls associated with Insurtech – the anticipated innovations in the insurance industry associated with social media marketing, artificial intelligence, big data analytics and more – and focuses in particular on the new methods of pricing and premium setting that are claimed to follow from the availability of self-tracking technologies and new volumes of customer data. Using the examples of telematics data in car insurance, efforts by health insurers to incentivize health behaviours (for example through the use of fitness trackers), and insurance companies’ own marketing materials, we assess the current state of play in the field of ‘personalized’ insurance pricing, pointing to obstacles as well as opportunities associated with its development. We then set these contemporary developments against a longer history of insurance pricing, understood as a history of arranging and classifying objects and persons for the purposes of calculating risk. We show that in its longer history, insurance reflected but also contributed to uncertainties about the distinction between persons and property. Drawing these two strands together, we conclude by assessing the implications of insurtech for future understandings of personhood. While there is scope for new categories of personhood to emerge, we show that there are also important continuities between past and present in terms of the challenge of bringing persons, parts of persons, material objects and pecuniary interests into successful alignment.

Calling attention to the growing intersection between the insurance and technology sectors-or 'insurtech'-this article is intended as a bat signal for the interdisciplinary fields that have spent recent decades studying the explosion of digitization, datafication, smartification, automation, and so on. Many of the dynamics that attract people to researching technology are exemplified, often in exaggerated ways, by emerging applications in insurance, an industry that has broad material effects. Based on in-depth mixed-methods research into insurance technology, I have identified a set of interlocking logics that underly this regime of actuarial governance in society: <i>ubiquitous intermediation, continuous interaction, total integration, hyper-personalization, actuarial discrimination</i>, and <i>dynamic reaction</i>. Together these logics describe how enduring ambitions and existing capabilities are motivating the future of how insurers engage with customers, data, time, and value. This article surveys each logic, laying out a techno-political framework for how to orient critical analysis of developments in insurtech and where to direct future research on this growing industry. Ultimately, my goal is to advance our understanding how insurance-a powerful institution that is fundamental to the operations of modern society-continues to change, and what dynamics and imperatives, whose desires and interests are steering that change. The stuff of insurance is far too important to be left to the insurance industry.

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

In the following article, the authors seek to understand if financial and insurance ecosystems can be further improved by using digital infrastructures and tools like digital platforms, trust services, identity blocks, and intelligent automation capabilities, combined with the use of insurance-oriented capital markets and corridor products that manage the direct engagement of the reinsurance industry with the primary risk markets. This research paper aims to cast light on the transformation mechanisms and to identify better power levers that contribute to faster adaptation to the developing situation of financial and insurance ecosystems that have become, in recent years, increasingly surrounding users who have adopted digital habits. It will also address how insurance risk management strategies can evolve to optimize the impact for interested stakeholders, the role of data and regtech in insurance, the preference for autonomous and scalable systems and necessary capabilities, the optimization of automation, and the monetary potential that could be achieved at the level of individual participants. The considered ecosystem perspective allows for the highlighting of the necessary components and the different roles of the digital platforms, trust services, identity blocks, and intelligent automation capabilities as contributing factors to greater business effectiveness, risk evaluation, and prevention measures, quality of the offered insurance concert, as well as potential financial risks and possible impacts on the reinsurance industry. The paper addresses various aspects of trust and quality of interaction within the financial and insurance ecosystems, considering digital components along four major axes: role and components of trust, characteristics of new generation digital platforms, business ecosystems, and digital capabilities in the structure and operation of reinsurance markets, trust anchors, trust services, and identity attributes. It also provides the insurance risk management strategy and the regtech portfolio.

Insurance plays a crucial role in human efforts to adapt to environmental hazards. Effective insurance can serve as both a measure to distribute, and a method to communicate risk. In order for insurance to fulfil these roles successfully, policy pricing and cover choices must be risk-based and founded on accurate information. This is reliant on a robust evidence base forming the foundation of policy choices. This paper focuses on the evidence available to insurers and emergent innovation in the use of data. The main risk considered is coastal flooding, for which the insurance sector offers an option for potential adaptation, capable of increasing resilience. However, inadequate supply and analysis of data have been highlighted as factors preventing insurance from fulfilling this role. Research was undertaken to evaluate how data are currently, and could potentially, be used within risk evaluations for the insurance industry. This comprised of 50 interviews with those working and associated with the London insurance market. The research reveals new opportunities, which could facilitate improvements in risk-reflective pricing of policies. These relate to a new generation of data collection techniques and analytics, such as those associated with satellite-derived data, IoT (Internet of Things) sensors, cloud computing, and Big Data solutions. Such technologies present opportunities to reduce moral hazard through basing predictions and pricing of risk on large empirical datasets. The value of insurers' claims data is also revealed, and is shown to have the potential to refine, calibrate, and validate models and methods. The adoption of such data-driven techniques could enable insurers to re-evaluate risk ratings, and in some instances, extend coverage to locations and developments, previously rated as too high a risk to insure. Conversely, other areas may be revealed more vulnerable, which could generate negative impacts for residents in these regions, such as increased premiums. However, the enhanced risk awareness generated, by new technology, data and data analytics, could positively alter future planning, development and investment decisions.

Islamic Fintech is a disruptive force in the financial sector, promising increased financial inclusion, economic development, and a plethora of efficient, transparent, Shariah-compliant financial solutions due to its integration of technology with Islam's profound ethical principles. This paper examines "Islamic Fintech," a swiftly expanding component of the global financial ecosystem that combines financial technology with Shariah principles derived from the Holy Qur'an and the Sunnah. This burgeoning industry seeks to align finance with Islamic ethical and moral codes, with an emphasis on the prohibition of usury, speculation, and investments in immoral goods. This paper also examines the origins, applications, and regulatory governance of Islamic Fintech. It highlights the breadth of its innovative applications, such as Shariah-compliant digital banking, crowdfunding, and P2P lending platforms, as well as the incorporation of cutting-edge technology such as AI and machine learning for risk assessment and investment strategies, blockchain technology for Islamic cryptocurrencies, and Insurtech for the streamlined distribution of Islamic insurance products. In addition, the paper elaborates on the necessity of regulatory frameworks and standardisation for the growth and sustainability of Islamic Fintech, highlighting the interoperability challenges.

The insurance industry is undergoing a profound digital transformation driven by the convergence of advanced machine learning (ML), InsurTech innovations, and scalable cloud data architectures. As insurers grapple with evolving customer expectations, increasing market competition, and complex risk landscapes, the adoption of AI-driven analytics and cloud-native platforms has become a strategic imperative. Advanced ML techniques—ranging from predictive risk modeling and personalized underwriting to real-time fraud detection and automated claims processing—are revolutionizing traditional insurance workflows, enabling data-driven decision-making with unprecedented accuracy and efficiency. Central to this transformation is the modern cloud data stack, which provides the scalable, flexible, and secure infrastructure necessary to manage vast volumes of structured and unstructured data. Key components, including cloud data lakes, real-time streaming platforms, orchestration pipelines, and AI-enabled analytics services, collectively empower insurers to derive actionable insights from diverse data sources, including IoT devices, telematics, and customer interaction channels. Moreover, the integration of MLOps practices ensures the seamless deployment, monitoring, and continuous improvement of ML models within agile cloud environments. However, the journey towards AI-first insurance ecosystems is not without challenges. Ensuring data privacy, regulatory compliance, model transparency, and cost-effective scalability are critical concerns that insurers must navigate. Additionally, overcoming legacy system constraints and fostering a culture of data-driven innovation remain pivotal for industry incumbents. This explores the interplay between advanced machine learning, InsurTech solutions, and cloud data stack architectures, highlighting practical applications, industry case studies, and emerging trends such as federated learning, serverless computing, and edge-based analytics. By harnessing these technologies in a cohesive, strategic manner, insurers can build resilient, customer-centric ecosystems that drive operational excellence, mitigate risks, and unlock new value streams in an increasingly digital insurance landscape.

The aim of this paper is to present and discuss the opportunities and risks related to implementation of new digital disruptive technologies in the operation of insurance companies. We compare theoretical knowledge and empirical evidence of six alternative approaches of digitalization implementation in insurance companies. We review trends and analyse the application of information technologies in risk management, in sales and distribution, Insurtech, big data and predictive analytics, Internet of things (IoT), telematics devices and Blockchain technology in insurance. Regardless of the threats related to digitalization processes and increasing number of "attackers" in the insurance sector, insurance companies are slowly and gradually embracing new technologies, adapting their strategies, organizational structures, risk management, employees and culture in order to add value to their companies and to the customers.

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The payment card industry has grown increasingly with the development of online business. However, payment card fraud has become a serious problem around the world. Companies and banks lost huge amounts of dollars annually due to fraud. It is necessary to investigate a learning algorithm to detect fraud in finance transaction automatically. In this paper, we put forward a fraud detection algorithm by using neural network. The neural network model and final result will be described to show the superiority of this model.

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

Consumer loans, i.e., loans to finance consumers to buy certain types of expenditures, is increasingly popular in e-commerce platform. Different from traditional loans with mortgage, online consumer loans only take personal credit as collateral for loans. Consequently, loan fraud detection is particularly critical for lenders to avoid economic loss. Previous methods mainly leverage applicant's attributes and historical behavior for loan fraud detection. Although these methods gain success at detecting potential charge-offs, yet they perform worse when multiple persons with various roles (e.g., sellers, intermediaries) collude to apply fraudulent loan. To combat this challenge, we consider the problem of loan fraud detection via exploiting roles of users and multi-type social relationships among users. We propose a novel Graph neural network with a Role-constrained Conditional random field, namely GRC, to learn the representation of applicants and detect loan fraud based on the learned representation. The proposed model characterizes the multiple types of relationships via self-attention mechanism and employs conditional random field to constrain users with the same role to have similar representation. We validate the proposed model through experiments in large-scale auto-loan scenario. Extensive experiments demonstrate that our model achieves state-of-the-art results in loan fraud detection on Alipay, one online credit payment service serving more than 450 million users in China.

Over the last 10 years, neural networks have been increasingly applied to various areas of finance. Neural networks are more often applied on the assets side than on the liabilities side of the balance sheet. Some major characteristics of the areas of these applications are their data intensity, unstructured nature, high degree of uncertainty, and hidden relationships. Most of the applications use the backpropagation model with one hidden layer. In most of these applications, neural networks out-performed traditional statistical models, such as discriminant and regression analysis. Furthermore, these applications have shown significant success in financial practice, for example, in forecasting T-bills, in asset management, in portfolio selection, and in fraud detection.

Recently, Internet finance is increasingly popular. However, bad debt has become a serious threat to Internet financial companies. The fraud detection models commonly used in conventional financial companies is logistic regression. Although it is interpretable, the accuracy of the logistic regression still remains to be improved. This paper takes a large public loan dataset, e.g. Lending club, for example, to explore the potential of applying deep neural network for fraud detection. We first fill the missing values by a random forest. Then, an XGBoost algorithm is employed to select the most discriminate features. After that, we propose to use a synthetic minority oversampling technique to deal with the sample imbalance. With the preprocessed data, we design a deep neural network for Internet loan fraud detection. Extensive experiments have been conducted to demonstrate the outperformance of the deep neural network compared with the commonly-used models. Such a simple yet effective model may brighten the application of deep learning in anti-fraud for Internet loans, which would benefit the financial engineers in small and medium Internet financial companies.

1. Logic Primer. The Logic Benefit. Sample Applications-Types of Uncertainty-A Fuzzy Set. A Case Study on Logic Inference. Financial Liquidity Evaluation Example-Conventional Decision Support Techniques-Linguistic Decision making. The Logic Algorithm. Fuzzification Using Linguistic Variables-Fuzzy Logic Inference Using If-Then Rules-Defuzzification Using Linguistic Variables. More Logic Theory. 2. Getting Started with fuzzyTECH for Business. Installation Guide. License Agreement-Installing fuzzyTECH and the Samples-Conventions-First Steps. Basic System Design Methodology. Using the Design Wizard-Creating a Rule Base- Interactive Debugging-File Debugging and Analyzers. Extending the System. Adding New Components-Interactive Debugging of Complex Projects-Advanced Features of fuzzyTECH-fuzzyTECH's Revision Control System-Creating Stand-Alone Solutions. 3. Getting Started with NeuroFuzzy Design. NeuroFuzzy Technology. Adaptive Systems and Neural Networks-Combining Neural and Fuzzy-NeuroFuzzy vs. Other Adaptive Technologies. Training Examples. Using the fuzzyTECH NeuroFuzzy Module-Training the Creditworthiness Evaluation-NeuroFuzzy Training in Data Analysis. Data Clustering. Clustering Techniques-Clustering with fuzzyTECH-Fuzzy Clustering of NeuroFuzzy Training Data. 4. Integration of Logic with Standard Software. Using DDE and DLL links with fuzzyTECH. Integration Link Overview-DDE Link-Programming fuzzyTECH Using the DLL Link. Integration of fuzzyTECH with MS-Excel. Installing the fuzzyTECH Assistant-Creating a Logic Spreadsheet-Stocks Analysis Case Study. Integration of fuzzyTECH with VisualBasic. Single Call Remote Interface Using VisualBasic-Standard Call Remote Interface Using VisualBasic-A Case Study Using VisualBasic. Integration of fuzzyTECH with MS-Access. Integration of Logic Functions-The FT Investment Bank Case Study-FT Investment Bank's MS-Access Database- AccessBasic Integration. 5. Case Studies of Logic Applications. Logic in Finance Applications. Scoring for Mortgage Applicants-Creditworthiness Assessment-Fraud Detection-Other Finance Applications. Logic in Business Applications. Supplier Evaluation for Sample Testing-Customer Targeting-Sequencing and Scheduling-Optimizing Research and Development Projects-Knowledge-Based Prognosis. Logic in Data Analysis Applications. Data Analysis in Cosmetics-Other Data Analysis Applications. 6. Advanced Logic Design Techniques. Linguistic Variables and Their Membership Functions. Design Methodology of Linguistic Variables-Linear Standard Membership Functions-Membership Function Shapes. Interfaces. Defining Interfaces-Building Explanatory Components. Inference Methods. Premise Aggregation with Logic Operators-Result Aggregation-Matrix Rule Representation. Defuzzification Methods. Best Compromise vs. Most Plausible Result-Comparison of Defuzzification Methods-Information Reduction by Defuzzification. 7. Bibliography. 8. Index.

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise.

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of [Formula: see text] for fraud and nonfraud classes. Other models like the Variational Quantum Classifier (VQC), Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimization strategies. However, challenges exist, including the need for more efficient quantum algorithms and larger and more complex datasets. This paper provides solutions to overcome current limitations and contributes new insights to the field of QML in fraud detection, with important implications for its future development.

Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones. The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains. In this article, a distributed approach of big data mining is proposed for financial fraud detection in a supply chain, which implements the distributed deep learning model of Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly. By training and testing on the continually updated SCF dataset, the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors, so as to enhance the financial fraud detection with high precision and recall rates, and reduce the losses of frauds in a supply chain.

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The rise of digital finance has led to a surge in fraudulent activities, particularly in credit card transactions and cryptocurrency ecosystems. With financial crimes becoming more sophisticated, traditional fraud detection methods often fail to identify complex fraudulent patterns. This research explores the application of machine learning (ML) and artificial intelligence (AI) techniques to enhance the security of digital finance by detecting fraudulent activities in credit card transactions and cryptocurrency wallets within the USA. The study utilizes large-scale transaction datasets containing key financial indicators such as transaction frequency, spending patterns, anomaly scores, and network behaviors. To develop an AI-driven fraud detection framework, we implement and compare six machine learning models: XGBoost, RLightGBM, Decision Trees, K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNNs), and Autoencoders. The models are trained on both structured financial data (e.g., credit card transaction logs) and unstructured blockchain transaction records (e.g., Bitcoin wallet addresses and transaction flows). To address data imbalance, the study applies the Synthetic Minority Over-sampling Technique (SMOTE), ensuring fair representation of fraudulent transactions. Model performance is evaluated using Precision, Recall, F1-score, and ROC-AUC metrics to determine the most effective fraud detection approach. Additionally, the research emphasizes data privacy and security, incorporating anonymization techniques and regulatory compliance measures to safeguard sensitive financial information. This study contributes to the ongoing fight against financial fraud by demonstrating how AI-based solutions can enhance the security and resilience of digital finance systems in the USA.

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Dianrong, a tech-driven internet finance company, provides loans to a large number of people and small business users. The ability to predict fraud from loan applications is key to the company's business. Based on published literature on fraud detection techniques, features have to be extracted manually for further rule design or machine learning. But as fraudulent behaviors change over time to avoid detection, simple features or rules become obsolete quickly. Normally, we have to extract hundreds of features, which is a time and resource consuming process. This paper proposes a new way to extract features automatically from a borrower's phone network graph using neural networks, which not only overcomes the above issue, but also captures features that are hard to fake. This method has yielded strong results in reality.

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Deep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it has been widely used in the financial area which is vital to the society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction is one of the most popular and valuable areas in finance. In this paper, it proposes a stock prediction model using Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) used as a generator that inputs historical stock price and generates future stock price and Convolutional Neural Network (CNN) as a discriminator to discriminate between the real stock price and generated stock price. Different from the traditional methods, which limited the forecasting on one-step-ahead only, by contrast, using the deep learning algorithm is possible to conduct the multi-step ahead prediction more accurately. In this study, it chose the Apple Inc. stock closing price as the target price, with features such as S&P 500 index, NASDAQ Composite index, U.S. Dollar index, etc. In addition, FinBert has been utilized to generate a news sentiment index for Apple Inc. as an additional predicting feature. Finally, this paper compares the proposed GAN model results with the baseline model.

Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems.

As one of the leading blockchain systems in operation, Ethereum has numerous smart contracts deployed to implement a variety of functions. Unfortunately, speculators introduce scams such as Ponzi scheme in the traditional financial sector into some of these smart contracts, causing millions of dollars of losses to investors. At present, there are a few of quantitative identification methods for new fraud modes under the background of Internet finance, and detection methods for the Ponzi scheme contracts on Ethereum are even less. In this paper, we propose an improved convolutional neural network as a detection model for Ponzi schemes in smart contracts. We use real smart contracts to evaluate the feasibility and usefulness of our mode. Results show that our improved convolutional neural network can overcome difficulties in training caused by different length of smart contracts' bytecodes. Compared with the state-of-the-art methods, the precision and recall rate of our model for Ponzi scheme detection are improved by 3.2% and 24.8% respectively.

The process of identifying abnormal objects or patterns that deviate from the typical behavior in a dataset or other observations is known as Anomaly Detection.It is an essential technique in many fields, such as cyber security, finance, transportation, and fraud detection.This paper combines an autoencoder and an isolation forest algorithm to enhance anomaly detection where the individual methods might not perform well due to the specific context and the nature of the dataset.The autoencoder is a neural network trained to reconstruct the input data, while the isolation forest is a tree-based algorithm that can identify outliers in the data.By combining these two methods, the autoencoder can learn a compact representation of the data, and the isolation forest can then be applied to the reconstructed data to identify anomalies.This combination effectively enhances the anomaly detection process in high-dimensional data when compared to utilizing individual algorithms.

With the rapid development of Internet finance, the volume of online transactions increases gradually, but the risk of exposure is increasing, and fraud is emerging. Because of the characteristics of online transaction, such as large volume, high frequency and fast update speed. In addition, online transaction data has the problems of unbalanced positive and negative sample and sparse timing of transaction data. Most of the existing methods to solve data imbalance are sampled, but this method will change the dataset’s distribution, which is not conducive to improving the generalization ability of the model. There are some timing characteristics of online transaction data, and the common fraud detection model does not take the problem into account in the design of the model. Based on the problems, this paper puts forward the siamese neural network structure based on CNN and LSTM, uses the siamese neural network structure to solve the problem of sample imbalance in online transaction and uses the LSTM structure to make model memory user's transaction information, in order to better detect the fraudulent transaction. The model presented in this paper is verified in real B2C transaction data, and its precision and recall reach about 95% and 96%, respectively.

One of the most serious ethical challenges in the credit card industry is fraud. Our paper’s major goal is to identify credit card theft and offer a reasonable solution to the problem. Credit card fraud has cost customers and banks billions of dollars around the world. Fraudsters are constantly attempting to come up with new ways and tricks to commit fraud, despite the fact that there are several measures in place to prevent it. Fraud detection is extremely important in the banking and finance industries. For detection purposes, we will use an artificial neural network. As a result, in order to prevent it, we will develop a system that will not only detect fraud, but will also detect it before it occurs. In order to detect new scams, our system will learn from previous frauds. Mining algorithms were used to detect fraud, but they failed miserably. We use machine learning methods to detect fraud in credit card transactions in our paper. The research employs supervised learning methods that are applied to a kaggle dataset that is severely skewed and imbalanced. We used robust scalar to balance the set, resulting in 51 percent non-fraud cases and 49 percent fraud ones. Logistic regression, random forest, decision tree, and KNN have all been implemented, with additional learning curves displaying which algorithm performs best. Accuracy, specificity, precision, and sensitivity are the evaluation criteria, and a comparative chart is created to show the comparative analysis of various supervised learning algorithms. KEYWORDS: KNN,Neural network,Logistic regression,Random forest,Decision tree

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise.This book reviews the state-of-the-art in current applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

As cyber threats and financial fraud continue to evolve, organizations are increasingly leveraging machine learning (ML) to enhance data security and detect fraudulent activities in real time. Traditional rule-based fraud detection systems struggle to adapt to sophisticated fraud patterns, necessitating the adoption of ML-driven approaches. This paper explores how machine learning algorithms improve fraud detection by analyzing large datasets, identifying anomalies, and mitigating security risks with greater accuracy and efficiency. The study examines various machine learning techniques employed in fraud detection, including supervised learning (e.g., logistic regression, decision trees, support vector machines), unsupervised learning (e.g., clustering, anomaly detection), and deep learning models (e.g., neural networks, autoencoders). These models enhance fraud detection by continuously learning from transactional data, reducing false positives, and improving detection rates. Feature engineering, data preprocessing, and model interpretability are also discussed as critical components in developing effective fraud detection systems. The integration of real-time analytics and artificial intelligence (AI) in fraud detection enables organizations to respond proactively to security threats. Techniques such as ensemble learning, reinforcement learning, and hybrid models further optimize fraud detection by combining multiple algorithms for higher accuracy. Additionally, big data analytics supports fraud detection by processing vast amounts of structured and unstructured data, improving decision-making speed and precision. Despite the advantages of machine learning in fraud detection, challenges such as data imbalance, adversarial attacks, and privacy concerns remain critical. This paper highlights strategies for addressing these challenges, including data augmentation, secure federated learning, and robust encryption techniques. Regulatory compliance and ethical considerations, such as bias in ML models, are also discussed to ensure responsible AI deployment in fraud prevention. Through case studies of ML-driven fraud detection in finance, e-commerce, and cybersecurity, this research demonstrates the effectiveness of intelligent fraud detection systems in safeguarding sensitive information and financial assets. Future research should explore the role of quantum computing and explainable AI (XAI) in advancing fraud detection technologies. By leveraging machine learning, organizations can enhance data security, improve fraud detection accuracy, and reduce financial losses, ensuring a more secure digital environment.

Fraud detection, a classical data mining problem in finance applications, has risen in significance amid the intensifying confrontation between fraudsters and anti-fraud forces. Recently, an increasing number of criminals are constantly expanding the scope of fraud activities to covet the property of innocent victims. However, most existing approaches require abundant historical records to mine fraud patterns from financial transaction behaviors, thereby leading to significant challenges to protect minority groups, who are less involved in the modern financial market but also under the threat of fraudsters nowadays. Therefore, in this paper, we propose a novel community-enhanced multi-relation graph neural network-based model, named CMR-GNN, to address the important defects of existing fraud detection models in the tail effect situation. In particular, we first construct multiple types of relation graphs from historical transactions and then devise a clustering-based neural network module to capture diverse patterns from transaction communities. To mitigate information lacking tailed nodes, we proposed tailed-groups learning modules to aggregate features from similarly clustered subgraphs by graph convolution networks. Extensive experiments on both the real-world and public datasets demonstrate that our method not only surpasses the state-of-the-art baselines but also could effectively harness information within transaction communities while mitigating the impact of tail effects.

Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.

The integration of Artificial Intelligence (AI) in the insurance sector has ushered in a new era of personalized insurance products, offering enhanced customer engagement and satisfaction. This review explores the transformative potential of AI in reshaping the landscape of insurance services, focusing specifically on the augmentation of customer engagement through personalized offerings. AI-driven algorithms and machine learning techniques enable insurers to analyze vast amounts of data with unprecedented speed and accuracy, facilitating the customization of insurance products to meet individual customer needs. By leveraging data from various sources such as IoT devices, social media, and historical claims data, insurers can gain deeper insights into customer behavior, preferences, and risk profiles. Personalized insurance products not only cater to the unique requirements of customers but also foster greater engagement by offering tailored recommendations, proactive risk management solutions, and real-time assistance. Through predictive analytics, AI algorithms can anticipate customer needs and preferences, allowing insurers to offer timely and relevant services, thereby enhancing customer satisfaction and loyalty. Moreover, AI-powered chatbots and virtual assistants serve as accessible and responsive touchpoints for customers, providing instant support, guidance, and personalized recommendations throughout the insurance lifecycle. By streamlining communication channels and offering seamless interactions, AI technologies strengthen the bond between insurers and customers, fostering long-term relationships built on trust and transparency. The integration of AI in personalized insurance products represents a transformative pathway towards enhanced customer engagement. By harnessing the power of AI-driven analytics and automation, insurers can deliver tailor-made solutions that resonate with individual customers, driving higher levels of satisfaction, loyalty, and ultimately, business growth. Keywords: Artificial Intelligence, Insurance, Privacy-Enhanced, Customer, Engagement, Review.

The recent report on FinTech to the European Commission by the Expert Group on Regulatory Obstacles to Financial Innovation (ROFIEG), of which the author was a member, noted that just 19 of the 161 largest retail and commercial businesses globally are implementing digital transformation at scale. 3 However, over the next 10 years, Europe will grow its FinTech market with existing and new players deploying Al, DLT, smart contracts, and quantum computing at scale. Al will radically transform the front, middle, and back offices of banks. However, as I conclude elsewhere,' the industry will have to manage its information architectures better if it is to fully leverage the potential of an Al to take data analytics to the next level or reduce the burgeoning costs of regulatory compliance. Significantly, innovations in smart contracts and DLT will transform the payment marketplace as they will enable crypt° assets of all types to be traded at all levels across markets. I expect that disruptive digital innovations based on the trading of cryptoassets will transform monetary and fi nancial systems. However, quantum computing with its potential to make strong Al a reality, and at a practical level to perform complex tasks, such as optimizing investment portfolios, identifying arbitrage opportunities, performing accurate credit and risks scoring, and so on, will be the fi nal step in the digital transformation of the industry. It is clear from the forgoing that no one technology is a silver ballet in digital transformation of financial institutions.

With the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

This research paper delves into the transformative possibilities of Artificial Intelligence (AI) within corporate finance, specifically focusing on its role in improving efficiency and decision-making processes. Through the utilization of machine learning, natural language processing (NLP), and robotic process automation (RPA), AI introduces innovative methods for enhancing corporate governance and sustainability practices. In the contemporary business landscape, corporations encounter mounting pressure to streamline operations while simultaneously addressing concerns regarding environmental, social, and governance (ESG) issues. Conventional finance methodologies often struggle to efficiently handle large volumes of data and extract actionable insights promptly. However, AI presents a shift in paradigm by enabling automated data analysis, recognizing patterns, and conducting predictive modeling, thus enabling finance professionals to make data-informed decisions swiftly and accurately. Machine learning algorithms play a pivotal role in detecting patterns and correlations within financial data, facilitating proactive risk management and strategic planning. Additionally, NLP technologies facilitate the extraction of valuable insights from unstructured data sources like regulatory filings, news articles, and social media, thereby enabling informed decision-making in corporate governance and sustainability endeavors. Moreover, RPA simplifies repetitive tasks and workflows, thereby reducing operational expenses and freeing up human resources for more strategic pursuits. Through the automation of routine processes such as data entry, reconciliation, and reporting, RPA enhances operational efficiency and ensures adherence to regulatory standards. Through the adoption of AI technologies, corporations can unlock novel avenues for innovation, optimize resource allocation, and promote sustainable growth within today's dynamic business milieu.

This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators.

Millions of uninsured individuals in the US live in the areas, which are highly vulnerable to health and other risks. Artificial intelligence (AI) has become one-point solution for a variety of socioeconomic, health, environmental, technological, and business challenges and insurance industry is not an exception. This article highlights how machine learning (ML) and deep learning (DL), the two pillars of AI, help to resolve a variety of challenges in the insurance industry.

Homeowners and renters insurance claims are numerous and variable. Insurance carriers have every intention to pay legitimate claims, yet HRI insurance claims present unique challenges because the insured's loss is often due to a perils insured against. It is precisely this unpredictability associated with perils insured against that creates the greatest opportunity for fraud. The urgency to relieve the insured of his or her distress generally short-circuits the process of investigation. Moreover, the norm within the industry is to settle first and investigate later. This environment creates a breeding ground for both opportunistic and organized fraud. A substantial portion of the claims activity in the HRI space represents organized fraud perpetrated by groups who, based on their prior experiences, believe they can navigate around the later-in-the-process investigation hurdles without consequence. If these fraudsters can bypass the case management system and the carrier's investigative staff, then in the months and years ahead, they will refile and settle again and again and again, with impunity. Internet search activity data provides unique insight into fraud risk. Search activity is a leading indicator of demand for many types of goods and services. Furthermore, the Internet search process is relatively frictionless. Estimates suggest that over 200 billion searches are conducted each year using major search engines; this number is rapidly growing. Consequently, the problem addressed in this paper is to develop a case management and decision support system that leverages existing online activity data to help insurance carriers identify potentially fraudulent HRI claims before settlement. As a response to the market need, the developed system provides a near real-time predictive infrastructure that allows for complementary offline predictive analytics and online predictive queries driven by state-of-the-art prediction models and predictive queries. Additionally, the proposed case management infrastructure allows for the development of both generic and customized backend and frontend case management modules that support claims processing across carriers.

Integrating Artificial Intelligence (AI) into the financial sector has revolutionized regulatory compliance, enabling organizations to navigate complex regulatory landscapes with enhanced efficiency and accuracy. This paper explores the transformative role of AI in Regulatory Technology (RegTech), focusing on how machine learning, natural language processing, and predictive analytics are automating critical compliance functions. These technologies empower financial institutions to detect fraudulent activities, monitor transactions, and identify non-compliance with unprecedented precision. By analyzing vast datasets, AI-driven tools can swiftly adapt to changing regulatory frameworks, mitigating risks and reducing operational costs. Through a comprehensive review of existing literature and case studies, this study highlights the practical applications of AI in areas such as Anti-Money Laundering (AML), sanctions compliance, and fraud detection. It further examines the challenges in deploying AI, including ethical considerations, data privacy concerns, and integrating AI systems into legacy infrastructures. While AI offers significant advantages, its adoption introduces complexities, such as ensuring transparency, managing biases, and balancing automation and human oversight. The paper concludes by emphasizing the necessity of collaborative efforts among stakeholders to address these challenges and develop robust governance frameworks. Looking ahead, advancements in AI promise to further enhance compliance operations, but this requires a commitment to ethical practices and continuous innovation. By responsibly leveraging AI, the financial sector can achieve more effective, adaptive, and sustainable compliance strategies, ultimately fostering trust and resilience in an increasingly regulated global market.

Unstructured text analytics has emerged as a powerful tool for transforming underwriting practices in the insurance industry. Traditional underwriting relies heavily on manual review of policy documents, proposals, and historical claims, which can be time-consuming, inconsistent, and prone to human error. Natural Language Processing offers advanced capabilities to extract, classify, and interpret information from unstructured text, enabling more accurate risk assessment and personalized underwriting decisions. This chapter examines how NLP techniques such as text mining, named entity recognition, sentiment analysis, topic modelling, and document classification can enhance the underwriting process by automating data extraction and identifying hidden patterns in policy and claims data. Intelligent text analytics supports insurers in identifying high-risk indicators, detecting fraudulent claims, assessing customer behaviour, and tailoring policy terms to individual risk profiles. While these advancements promise improved efficiency and decision quality, they also introduce challenges related to data quality, model interpretability, regulatory compliance, and ethical considerations. Through a conceptual analysis of emerging research and industry practices, this chapter highlights the transformative potential of NLP-driven unstructured text analytics in developing personalized underwriting frameworks. It concludes with recommendations for practitioners and identifies opportunities for future research in responsible and scalable AI adoption in insurance operations.

Abstract Recent advances in large language models (LLMs), such as GPT-4, have spurred interest in their potential applications across various fields, including actuarial work. This paper introduces the use of LLMs in actuarial and insurance-related tasks, both as direct contributors to actuarial modelling and as workflow assistants. It provides an overview of LLM concepts and their potential applications in actuarial science and insurance, examining specific areas where LLMs can be beneficial, including a detailed assessment of the claims process. Additionally, a decision framework for determining the suitability of LLMs for specific tasks is presented. Case studies with accompanying code showcase the potential of LLMs to enhance actuarial work. Overall, the results suggest that LLMs can be valuable tools for actuarial tasks involving natural language processing or structuring unstructured data and as workflow and coding assistants. However, their use in actuarial work also presents challenges, particularly regarding professionalism and ethics, for which high-level guidance is provided.

The emergence of AI technologies in the finance industry has become increasingly prominent due to the growing prevalence of data. Recent advancement of AI technology and big data can notably improve efficiency in fund allocation and financial risk management, which eventually give rise to new opportunities for wealth management firms. The emergence of automatic trading has opened a new world for individuals with little financial investment ability. However, the wealth-management services that financial consulting companies offer still have high thresholds and fees, and are often not reachable by ordinary people. Startups of wealth management attempting to bridge the gap have emerged in China to provide services based on the normal users' credit data from ecommerce and network service providers. Nevertheless, the investment experts involved in the startups usually rely on prior knowledge and financial heuristics to go through their task instead of sophisticated intelligent methods. Advances in AI technologies such as automatic style recognition and natural language processing can allow machines to replace laborers to provide interactive services to customers. These technologies have the potentials to facilitate the subsequent work of finance and big data to bridge the gap by combining financial intelligence with AI in the construction of a financial brain, which refers to an inclusive intelligent toolbox supplying kinds of traditional professional financial services/quasi-financial services to meet the various financial needs of ordinary people. The security and reliability of making money that are as important as investment-style match, must remain at the forefront of consideration. Comprehensive analysis to weigh certain chosen intelligent and classic technologies should be exploited to construct models and products.

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Recently some techniques (such as statistical techniques and machine learning techniques) have been developed for evaluating individual credit information to decide whether the person meets the criteria of credit financing, and the process is known as credit scoring. This paper mainly focuses on the comparative assessment of the performances of five popular classifiers involved in machine learning used for credit scoring: Naive Bayesian Model, Logistic Regression Analysis, Random Forest, Decision Tree, and K-Nearest Neighbor Classifier. Each classifier has its own strength and weakness, it is assertive to say which one is the best. However, the results of this experiment pinpoint that Random Forest performs better than others in terms of precision, recall, AUC (area under curve) and accuracy.

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A precise credit risk assessment system is always vital to any financial institution for impeccable and gainful functioning. In such an ever-changing economy as the rate of loan defaults are gradually increasing, authorities of financial institutions are finding it more and more difficult to correctly assess loan requests and tackle the risks of loan defaulters. In light of these events this paper proposes a machine learning model which can precisely assess credit risk and predict possible loan defaulters for credit lending institutions. A comparative analysis has been made using tuned supervised learning algorithms such as Support Vector Machine, Random Forest, Extreme Gradient Boosting and Logistic Regression for identifying defaulters. Recursive Feature Elimination with Cross-Validation and Principal Component Analysis have been used for dimensionality reduction. Metrics such as F1 score, AUC score, prediction accuracy, precision and recall have been used to evaluate each model. Among all the models, the combination of a tuned Support Vector Machine and Recursive Feature Elimination with Cross-Validation have shown great promise in identifying loan defaulters. The proposed model, therefore, can assist financial institutions in accurately identifying loan defaulters and prevent them from incurring further loss.

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Financial institutions suffer from risk of losing money from bad customers. Specifically banking sectors where the risk of losing money is higher, due to bad loans. This causes economic slowdown of the nation. Hence credit risk assessment is an important research area. In this paper research methodology based framework using diagnostic, cross sectional study is used for risk analysis. Empirical approach is used to build models for credit risk assessment with supervised machine learning algorithms. Two classification and prediction models are built one using Logistic regression and other using Neural Network. Models are evaluated using chi square statistical test. This study infers the significance of using machine learning algorithms in predicting bad customers. For the data set and parameters considered logistic regression has shown better performance.

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy.

Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics.

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In the rapidly evolving landscape of financial technology, machine learning algorithms are increasingly supplanting traditional methodologies for evaluating consumer credit risk. This study leverages a comprehensive dataset comprising 10,000 credit accounts to conduct a comparative analysis of four prevalent machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). The results distinctly favor GBM, which achieves an AUC of 0.87, closely followed by Random Forest with an AUC of 0.85. In stark contrast, Logistic Regression and Decision Tree recorded lower AUCs of 0.78 and 0.72, respectively. GBM and Random Forest significantly outperform in classification accuracy, attaining 92% and 90%, respectively, far exceeding the 86% by Logistic Regression and 80% by Decision Tree. Notably, GBM exhibited 95% specificity and 90% sensitivity, efficiently identifying high-risk accounts while minimizing false positives among low-risk categories. Furthermore, the study delves into the handling of imbalanced datasets, interpretability, and computational demands of each algorithm, offering quantifiable insights that inform future directions for optimizing credit risk models, particularly in enhancing transparency and scalability.

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Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Credit risk assessment is acting as a survival weapon in almost every financial institution. It involves deep and sensitive analysis of various financial, social, demographic and other pertinent data provided by the customers and about the customers for building a more accurate and robust electronic finance system. The classification problem is one of the major concerned in the process of analysing gamut of data; however, its complexity has ignited us to use machine learning–based approaches. In this paper, some machine learning algorithms have been studied and compared their effectiveness for credit risk assessment. Further, as an extension of our study, we develop a novel sliding window–based meta–majority voting ensemble learning to improve the prediction accuracy of credit risk assessment problem by properly analysing the underlying samples. The experimental findings draw a clear line between the proposed ensembler and traditional ensemblers. Moreover, the proposed method is very promising vis–à–vis of individual classifiers.

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Credit risk evaluation innovation is of incredible importance to monetary establishments. AI innovation can fundamentally work on the precision and versatility of credit risk evaluation. This paper aims to study the risk assessment of operator big data Internet of Things credit financial management based on machine learning. It proposes machine learning-related algorithms, including the introduction of logistic model and decision tree model, as well as related concepts of credit financial management risk. This paper proposes that big data can be better used to reduce financial risk management problems and proposes specific actions based on the actual situation of the company. This paper selects company A for financial risk management evaluation through case analysis and compares it with three major e-commerce companies. The experimental results show that the earnings per share of company A is between −0.99 and 0. Company A is still in a state of loss in recent years, and there are certain debt risks, operational risks, and capital risks.

Abstract Making responsible lending decisions involves many factors. There is a growing amount of research on machine learning applied to credit risk evaluation. This promises to enhance diversity in lending without impacting the quality of the credit available by using data on previous lending decisions and their outcomes. However, often the most accurate machine learning methods predict in ways that are not transparent to human domain experts. A consequence is increasing regulation in jurisdictions across the world requiring automated decisions to be explainable. Before the emergence of data-driven technologies lending decisions were based on human expertise, so explainable lending decisions can, in principle, be assessed by human domain experts to ensure they are fair and ethical. In this study we hypothesised that human expertise may be used to overcome the limitations of inadequate data. Using benchmark data, we investigated using machine learning on a small training set and then correcting errors in the training data with human expertise applied through Ripple-Down Rules. We found that the resulting combined model not only performed equivalently to a model learned from a large set of training data, but that the human expert’s rules also improved the decision making of the latter model. The approach is general, and can be used not only to improve the appropriateness of lending decisions, but also potentially to improve responsible decision making in any domain where machine learning training data is limited in quantity or quality.

Credit risk assessment and fraud detection are crucial tasks in the financial industry, vital to preserving financial organizations' legitimacy and sustainability. Traditional methods often fall short in accurately assessing risk and detecting fraudulent activities in a timely manner. In recent years, machine learning has emerged as a powerful tool for enhancing these processes, leveraging great dimensions of transactional statistics and superior algos for making more informed decisions. This research paper explores the usage of ML techniques in credit risk assessment and fraud detection within financial transactions. The paper begins with an overview of the importance of accurate risk assessment and fraud detection in financial transactions and introduces the role of machine learning in addressing these challenges. A comprehensive literature review is conducted to analyze existing methodologies, algorithms, and research trends in the field. Data acquisition and preprocessing techniques are discussed, emphasizing the importance of clean and relevant data for model training. Feature engineering strategies are explored to extract meaningful information from financial transaction data and enhance the predictive capabilities of machine learning models. Various machine learning algorithms suitable for credit risk assessment and fraud detection are examined, including LR, SVMs, RF, DTs and DNNs. The efficacy of these techniques is evaluated by discussing model metrics for assessment and ensemble approaches for boosting efficiency, with a focus on metrics such as accuracy, precision, recall, and ROC-AUC. The paper presents case studies and experimental results illustrating the application of machine learning models in real-world scenarios, highlighting their effectiveness in improving risk assessment and fraud detection processes. Additionally, difficulties such as imbalanced datasets, comprehensibility of the model and adherence to regulations are discussed, along with potential research directions and future trends in the field. In conclusion, this research emphasizes the transformative potential of machine learning in credit risk assessment and fraud detection within financial transactions. By leveraging advanced algorithms and data-driven approaches, financial institutions can enhance their decision-making processes, mitigate risks, and safeguard against fraudulent activities, ultimately contributing to a more secure and resilient financial ecosystem.

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In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.

This paper investigates the application of Machine Learning for credit risk assessment in Multichain Decentralized Finance (DeFi). With DeFi expanding its scope, the need for effective credit risk evaluation becomes paramount. Our study utilizes a diverse dataset gathered from multiple blockchains, including Ethereum, and employs rigorous data preprocessing techniques. DeFi-specific features are extracted, capturing transaction-related statistics. Machine learning models, such as Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM and a CNN, are deployed to predict wallet liquidations. Evaluation metrics, including accuracy, ROC curve and Area Under the Curve, demonstrate the efficacy of DeFi-related features in credit risk assessment. Furthermore, we analyze feature importance and inter-feature correlations, providing insights into critical risk factors within the DeFi ecosystem. This research contributes valuable insights to the DeFi landscape, offering data-driven approaches to credit risk management and investment strategies. Our findings hold significance for DeFi stakeholders seeking to navigate the evolving financial frontier while mitigating credit risk effectively.

Abstract Recent research using explainable machine learning survival analysis demonstrated its ability to identify new risk factors in the medical field. In this study, we adapted this methodology to credit risk assessment. We used a comprehensive dataset from the Estonian P2P lending platform Bondora, consisting of over 350,000 loans and 112 features with a loan volume of 915 million euros. First, we applied classical (linear) and machine learning (extreme gradient-boosted) Cox models to estimate the risk of these loans and then risk-rated them using risk stratification. For each rating category we calculated default rates, rates of return, and plotted Kaplan–Meier curves. These performance criteria revealed that the boosted Cox model outperformed both the classical Cox model and the platform’s rating. For instance, the boosted model’s highest rating category had an annual excess return of 18% and a lower default rate compared to the platform’s best rating. Second, we explained the machine learning model’s output using Shapley Additive Explanations. This analysis revealed novel nonlinear relationships (e.g., higher risk for borrowers over age 55) and interaction effects (e.g., between age and housing situation) that provide promising avenues for future research. The machine-learning model also found feature contributions aligning with existing research, such as lower default risk associated with older borrowers, females, individuals with mortgages, or those with higher education. Overall, our results reveal that explainable machine learning survival analysis excels at risk rating, profit scoring, and risk factor analysis, facilitating more precise and transparent credit risk assessments.

The study is aimed at assessing and managing the green credit risk of banks, reduces the systemic risk in the financial industry, and improves the efficiency of the use of bank funds. With the development and evolution of efficient wireless data communication and transmission technology, the study combines theoretical and empirical green credit analysis to analyze listed companies in different industries quantitatively. The index system of credit risk assessment is established through wireless data transmission technology combined with mobile computing and machine learning neural networks. A back‐propagation neural network (BPNN) model is confirmed by principal component analysis and factor analysis, and the performance of the model is verified with example data. The results show that the BPNN‐based credit risk assessment model can provide 95% accuracy. In addition, 99% of the sample companies have low risk and no green credit risk. However, most companies in the coal industry are at greater risk. Overall, medium and high‐risk companies accounted for 11.5%. Compared with other state‐of‐the‐art models, the machine learning neural network adopted here has better data fitting and prediction accuracy, higher learning efficiency, and higher accuracy. The model established inefficient wireless communication is suitable for bank credit risk assessment and has good reference value and practical significance for bank credit risk assessment and management in different industries.

Bank rating prediction can be important for financial environment in any country. With the complex dimension of datasets, it requires effective dimensionality reduction method and high accurate model. Among machine learning, recursive feature selection, lasso regression and principal component analysis (PCA) may have better ability in dimensionality decrease. And supervised learning includes random forest, SVM and artificial neural network attract great attention by high accuracy and powerful adaptation. Our study presents a combinational feature selection procedure by using the lasso and recursive feature elimination, in order to Figure out the key features among the bank credit rating prediction. Three important features are extracted and applied to the training of random forest, SVM and gradient boosted classification. Finally, SVM model gain the best accuracy of 86% in validated dataset. In addition, in order to evaluate the uncertainty provided by complex and intricate finical environment, we apply proportional hazards model (COX) and Kaplan-Meier to explain the effect of each variables. We measure the possible relationship between a company's daily revenue and its credit ranking. It implies that the days of zero income and negative income may affect the company's credit rating.

In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions. Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. The effectiveness, efficiency, and robustness of the proposed TSC-SVM model are demonstrated through various real-world applications. The proposed algorithm is anticipated to expand the customer base for financial institutions while reducing economic losses.

The examination of credit risk has become very crucial in the financial world to avoid massive losses, as, without repayment of loans, they earn no profit. It can be thought of as an expansion of the credit distribution measure. To ease the task of investors, a methodology is proposed using machine learning models that can predict the status of whether the loan should be granted to a customer based on his pre-fed attributes. first, preprocessing of the data is performed followed by feature extraction using LDA and PCA. The model is created using various machine learning algorithms on two different sized datasets. It has been observed that Logistic regression shows the highest accuracy followed by random forest classification and KNN. It is also seen that LDA performed better than PCA in all algorithms. Therefore, machine learning regression and classification algorithms have shown reliable results for the money-lenders to safely invest.

Since the outbreak of the COVID-19, small and medium-sized enterprises have been greatly affected. In order to cope with the difficulty of capital turnover for small and medium-sized enterprises, the government has successively introduced a series of financial policies to increase credit support and reduce financing costs. The rapid development of technology has also prompted further innovations in the operating models of banks and other credit platforms. However, banks and credit platforms must consider practical issues such as their own capital costs and risk assessment while they help small and medium-sized enterprises reduce financing costs. This paper aims to study and design a credit risk assessment system based on big data technology and machine learning algorithms. It is hoped that the system will enhance the bank's ability to identify the credit risks of small and medium-sized enterprises, so as to solve the problem of difficult and expensive financing for small and medium-sized enterprises. At the same time, it will reduce the bank's own bad loan ratio and increase profit margins. Achieving a win-win situation for small and medium-sized enterprises and banks, it's crucial to promote jointly the development of economy.

For lending organizations, determining a borrower’s creditworthiness is essential to determining their capacity to repay loans. The prediction of credit scores using feature engineering and machine learning techniques is the main emphasis of this study. Using the Kaggle Family Credit Default Risk dataset, the AUC scores of several machine learning models are compared. Modern machine learning techniques, including well-established methods like Random Forest and Linear Support Vector Machines, can be effectively applied to credit scoring. Ensemble models, such as LightGBM, offer advantages like improved predictions and increased stability, making them well-suited for this specific use case. Combining predictions from multiple models often results in less noisy outcomes compared to using a single model, outperforming other techniques like XGBoost, SVMs, and logistic regression.

This study presents a novel Early Warning System for monitoring the credit risk of commercial customers at a large international bank headquartered in the Netherlands. Traditional early warning methods often rely on backward-looking indicators such as probability of default or loss given default, which can limit predictive performance. To address this, we investigate the effectiveness of a Watchlist-based trigger for forecasting financial distress and adverse customer migration. We assess its precision, timeliness, and sensitivity across different client status transitions. Using a rich dataset combining internal banking records and external financial information, we implement and compare several machine learning algorithms, including Linear Discriminant Analysis, Logistic Regression, Decision Trees, Support Vector Machines, Random Forest, Gradient Boosting, Extreme Gradient Boosting, and Artificial Neural Networks. To enhance model transparency and support adoption, we employ SHapley Additive exPlanations to interpret key predictors of risk. Among all models, Random Forest achieves the highest performance, demonstrating strong F1 scores, superior trigger precision, and high sensitivity to migration. It successfully anticipates 12.7% of negative client transitions and helps prevent 67.6% of cases that would otherwise result in financial losses for the bank. This research contributes a data-driven, explainable solution for proactive credit risk management and offers actionable insights to support strategic decision-making in commercial banking. • Develop a machine learning model to predict credit risk in commercial banking. • Evaluate early warning triggers for detecting financial distress. • Demonstrate Random Forest as the most effective risk prediction model. • Utilize SHAP values to enhance model explainability in risk analysis. • Support data-driven decision-making for credit risk management.

Predicting the risk while lending money has always been a challenge for financial institutions. To make such decisions many banks or financial organizations follow different techniques to analyze a set of data. Manual prediction and analysis of credit risk can not only be very hectic but also quite time-consuming. To solve this issue, what is needed is a system that ensures high predictive accuracy and optimality. Machine Learning algorithms such as various Regression models, Gradient Boosting, Deep Learning, Neural Networks, Ensemble Learning and others can be used to anticipate whether a consumer is eligible for taking a loan with high accuracy. In this paper, an attempt has been made to find a good ML algorithm that shall help various banks and/or financial institutions to reliably predict the credit risk on an individual by analyzing appropriate datasets. Following that, a highly accurate result for said institutions can be ensured, which they can use to determine whether a consumer requesting credit should be allotted credit or not.

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Information on borrower quality is a fundamental issue in debt contracting, corporate and consumer finance, and financial intermediation. We investigate the link between account activity and information production on borrower risk. Based on a unique data set, we find that credit line usage, limit violations, and cash inflows exhibit abnormal patterns approximately 12 months before default events. Measures of account activity substantially improve default predictions and are especially helpful for monitoring small businesses and individuals. Furthermore, early warning indications result in higher loan spreads, and in a higher likelihood of limit reductions and complete write-offs. Our study shows that account activity provides a real-time window into the borrower's cash flows, thus explaining why banks have an advantage in providing certain types of debt financing. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

This paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these frameworks enhance banking efficiency and accuracy. It discusses various AI techniques such as machine learning algorithms, neural networks, and natural language processing, and their application in credit risk assessment. Furthermore, it examines the challenges and opportunities presented by these frameworks, highlighting their potential to revolutionize the banking sector. Revolutionizing Credit Risk Assessment in Banking, The Role of Artificial Intelligence In the dynamic realm of finance, the assessment of credit risk stands as a fundamental pillar for banking institutions. Traditionally, this process has heavily relied on statistical models and historical data. However, the emergence of Artificial Intelligence (AI) has catalyzed a transformative shift in this domain. This paper elucidates the theoretical underpinnings of AI frameworks employed in credit risk assessment and investigates their profound implications for enhancing the efficiency and accuracy of banking operations. The exploration begins by delineating various theoretical frameworks in AI pertinent to credit risk assessment. Leveraging machine learning algorithms, neural networks, and natural language processing techniques, these frameworks offer innovative approaches to evaluate creditworthiness. Unlike conventional methods, AI-driven models possess the capacity to ingest vast datasets, identify intricate patterns, and adapt dynamically to evolving market dynamics. Such capabilities empower banks to make more informed and timely decisions regarding lending activities. Moreover, this paper delves into the practical application of AI techniques in credit risk assessment. Through case studies and empirical evidence, it elucidates how these advanced methodologies enable banks to mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize credit scoring processes, identify potential defaulters with greater accuracy, and customize lending terms based on individual risk profiles. Additionally, AI facilitates real-time monitoring of credit portfolios, allowing proactive risk management and timely interventions to prevent adverse outcomes.

Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.

Plenty of research focuses on supply chain finance and its risk, qualitatively or quantitatively. However, there are only a little literature studies on the Internet supply chain finance (ISCF), especially on its risk by quantitative analysis. After analyzing the information of partners' panorama data and upstream and downstream data in the internet supply chain, this paper constructs a multiple dimensional intelligent risk assessment system for ISCF. By using the analytic hierarchy process gray assessment theory, a risk assessment model of ISCF is built. Based on the collected big data, tracking and monitoring partners' panoramic data, and upstream and downstream data of the supply chain in real time, the risk of ISCF can be calculated through the assessment model, so the investor can decide whether to finance or not before lending and monitor the lender dynamically after loaning. Taking Zhong-ken Supply Chain Co., Ltd., a focal company in the supply chain, as an example, this paper evaluates the risk of lending to one financing enterprise and obtains a specific risk value, by which to describe the risk degree. Therefore, the model has certain practicability.

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

ABSTRACT Peer-to-peer (P2P) lending enables individuals and small companies to finance and invest without the intermediation of financial institutions. However, this business model is also associated with high delinquency risk and a lack of risk monitoring and control capabilities. This paper explores the potential of the Internet of Things (IoT), blockchain, smart contract technologies, and the Continuous Risk Monitoring and Assessment (CRMA) framework to re-engineer risk monitoring and control for P2P lending. We conducted a case study of a large Chinese P2P lending company to identify problems in its current risk monitoring and control processes and to design an IoT-smart contract CRMA system to continuously monitor and respond to delinquency risk via real-time data collection, automatic loan settlement, and in-time risk disclosure. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M49.

Robotic Process Automation (RPA) has emerged as a transformative technology within the finance sector, offering significant advancements regulatory compliance by automating complex reporting and auditing processes. In an increasingly stringent regulatory environment, financial institutions face growing challenges in meeting compliance requirements that demand accuracy, timeliness, and transparency. Traditional compliance workflows, heavily dependent on manual interventions, are often prone to errors, inefficiencies, and high operational costs, exposing organizations to financial penalties and reputational risks. RPA addresses these challenges by leveraging software robots to execute rule-based tasks consistently, thereby reducing manual errors and enhancing the efficiency of compliance operations. Through the automation of regulatory reporting, RPA ensures that large volumes of financial data can be consolidated, validated, and submitted accurately within prescribed timelines. This reduces the risk of non-compliance while freeing compliance officers to focus on strategic oversight and risk management. In auditing, RPA provides real-time monitoring capabilities by creating automated audit trails that record every transaction and process step, enhancing transparency and accountability. Furthermore, integration with data analytics enables financial institutions to detect anomalies and irregularities swiftly, supporting proactive risk identification and mitigation. By embedding RPA into compliance frameworks, institutions can align more effectively with international standards such as IFRS, Basel III, and anti-money laundering directives. The strategic implications of RPA extend beyond operational improvements, as its adoption strengthens corporate governance, builds stakeholder confidence, and fosters resilience in the face of evolving regulatory demands. However, successful implementation requires robust governance structures, clear process mapping, and ongoing monitoring to ensure that automation aligns with legal requirements and ethical standards. RPA, therefore, not only provides a mechanism for reducing compliance costs and risks but also positions financial institutions as proactive leaders in accountability and innovation. In summary, RPA ensures regulatory compliance in finance by automating complex reporting and auditing processes, enhancing accuracy, reducing risks, and enabling financial institutions to meet global regulatory expectations with efficiency and transparency.

Purpose The increase of turbulence sources and risk points under the complex social information network has brought severe challenges. This paper discusses risk perception and intelligent decision-making under the complex social information network to maintain social security and financial security. Design/methodology/approach Cross-modal semantic fusion and social risk perception, temporal knowledge graph and analysis, complex social network intelligent decision-making methods have been studied. A big data computing platform of software and hardware integration for security combat is constructed based on the technical support. Findings The software and hardware integration platform driven by big data can realize joint identification of significant risks, intelligent analysis and large-scale group decision-making. Practical implications The integrated platform can monitor the abnormal operation and potential associated risks of Listed Companies in real-time, reduce information asymmetry and accounting costs and improve the capital market's ability to serve the real economy. It can also provide critical technical support and decision support in necessary public opinion monitoring and control business. Originality/value In this paper, the theory of knowledge-enhanced multi-modal multi-granularity dynamic risk analysis and intelligent group decision-making and the idea of an inference think tank (I-aid-S) is proposed. New technologies and methods, such as association analysis, time series evolution and super large-scale group decision-making, have been established. It's also applied in behavior and situation deduction, public opinion and finance and provides real-time, dynamic, fast and high-quality think tank services.