金融风险

中小企业融资难是中小企业发展过程中的一大难题，同时也是金融机构和企业共同面临的一个难题。由于中小企业本身规模小、数据不全面、信息不透明，导致在金融服务方面存在着很大的风险，致使融资难成为了制约中小企业发展的瓶颈。大数据技术的发展，为解决中小企业融资难提供了新的思路，借助大数据技术可以有效解决中小企业融资难的问题。本文通过分析大数据背景下中小企业金融风险控制中存在的问题，并在此基础上提出了几点应对策略，以期能够为大数据背景下中小企业金融风险控制提供一些参考，为缓解融资难问题提供新的思路。

 影子银行通过资产证券化的运作机制，发行高杠杆的结构化衍生产品，规模快速扩张，已渗透到整个金融市场，但难以受到严格监管。一旦形成局部系统性金融风险，风险会蔓延至整个金融系统。本文基于国内影子银行现状，分析其与系统性金融风险的关系，提出构建全面监管体系、强化风险管理与宏观审慎政策协同、构筑风险防控体系相关政策建议，以加强监管、提升风险管理能力，确保金融市场稳定与健康发展。

随着互联网金融的快速发展，传统风险管理方法已难以应对其复杂多变的风险特征，金融工程技术在风险控制中的应用日益重要。本文系统分析了金融工程的基本概念及其在互联网金融风险控制中的重要作用，深入探讨了金融工程在风险识别、风险评估、风险控制和风险监控四个阶段的具体应用。研究表明，金融工程通过运用机器学习、数据挖掘、实时计算等先进技术，能够显著提高风险识别的精准性和风险控制效率，丰富风险管理方式。当前各大互联网金融机构已广泛采用相关技术构建智能化风险管理体系，取得良好效果。未来需要进一步加强新兴技术融合，完善风险模型，推动行业风险管理水平整体提升。

人性的贪婪和欲望永无止境，美国严格保护平台员工权益，探索适用互联网平台的新反垄断理论，防止平台侵犯中小商家利益，加强监管金融资本，反而使美国电商平台可持续发展；中国已经开始对电商平台开展反垄断执法，但电商平台侵犯广大员工权益、损害中小商家利益等行为并未受到有效遏制，依旧享受很多国家扶持政策，虽然规模加速扩张，财富效应却呈现边际效应递减趋势，平台股东在减持套现，金融风险在持续增加。 Human greed and desire are boundless. The United States strictly protects the rights and interests of platform employees, explores new antitrust theories applicable to internet platforms to prevent them from infringing on the interests of small and medium-sized businesses, and strengthens the regulation of financial capital. These measures have instead contributed to the sustainable development of U.S. e-commerce platforms. In contrast, China has begun antitrust enforcement against e-commerce platforms, but violations of employee rights and harm to small and medium-sized businesses have not been effectively curbed. These platforms still enjoy substantial government support. Although their scale is expanding rapidly, the marginal effect of wealth creation is diminishing, platform shareholders are reducing their holdings and cashing out, and financial risks continue to rise.

21世纪是继区块链技术与供应链金融融合后迅速发展的时代，区块链因其去中心化、去信任化，被人们广泛运用在金融风险中。文中主要通过区块链技术来分析金融中的风险，并针对风险情况做出合理的防范对策。重点介绍了区块链技术与金融风险之间的关系，以及区块链技术在金融领域的应用，如风险成因，以及对应防范策略，并通过传统方差法和金融市场风险价值法进行风险计量，主要在安全性风险、流动性风险以及收益率三方面进行对比，结果发现，基于区块链技术的风险价值法相对传统风险评估方法风险性降低了0.2，偏差较小，波动性风险较低，能比较准确的评估金融存在的风险，且最终的收益率也高出8%左右。文中在区块链技术预防金融风险方面有着重要的参考意义。

随着气候变化日益成为系统性金融风险的重要来源,如何科学评估气候相关金融风险 成为学术界与监管机构共同关注的前沿议题。本文采用文献综述方法,系统梳理了气 候相关金融风险评估方法的研究进展。首先,界定了气候相关金融风险的内涵,明确 区分物理风险与转型风险,并阐释其传导机制。其次,构建了涵盖评估范式、风险类 型与应用主体的三维分类体系,对情景分析与压力测试、计量经济模型、投入产出模 型、机器学习方法、披露分析法等主流评估方法进行述评,比较其适用场景、数据要 求与优缺点。再次,从国际实践、国内进展与方法有效性三个维度总结方法应用现状 。研究发现,当前评估方法呈现多元化发展态势,但仍面临数据可得性不足、模型不 确定性高、多风险叠加复杂等挑战。未来研究应聚焦于跨学科方法融合、动态评估 框架构建以及评估标准的统一协同,以提升气候金融风险评估的科学性与实践应用价 值。

在现代化社会发展中，金融经济已然成为国民经济中的核心部分，其发展态势直接影响着国家整体经济发展。而随着经济全球化趋势的不断加强，金融经济领域中的竞争也越来越激烈，如何在竞争激烈的市场环境中，实现风险的有效控制，促进金融经济稳定发展，已成为各金融机构首要思考的问题。金融法律监管作为金融风险控制的重要手段，对于金融风险的有效规避和防范具有重要作用。本文简单阐述了金融风险的概念及其特点，对金融法律监管在金融风险控制中的作用进行了分析，并在此基础上，探讨了金融法律监管完善的有效策略，以供参考。

当一国依赖数字金融基础设施以维持经济社会运行时，在外汇枯竭、能源短缺等宏观危机冲击下，就会出现从“数字联通”滑向“数字依存”的险境。尼泊尔经济危机中潜在因素之一正是数字支付系统的崩溃。数字支付系统作为关键数字基础设施是一把双刃剑：它在平时提升效率，在危机时却可能成为系统性风险的放大器与传导器。研究认为，尼泊尔的经济危机揭示了泛喜马拉雅地区“数字丝绸之路”建设中的一个潜在风险：区域数字互联互通的深化，在缺乏协同治理与风险缓冲机制的情况下，可能使成员国间的“数字脆弱性”相互传导，形成区域性的数字风险链条。因此，构建区域“数字韧性”应成为周边公共治理的新议程。

本文探讨了系统性风险对金融市场的深远影响，并分析了量化策略在风险管理中的应用。通过综述现有研究进展，揭示了系统性风险的多维复杂特性及其对金融体系的冲击。量化策略实证分析进一步验证了其对风险识别、量化评估及高效管理能力，成为应对系统性风险的关键工具，提升了市场稳定性与韧性。然而，LTCM 案例也揭示了量化策略在高杠杆操作环境下的脆弱性以及对模型风险的敏感性和局限性，未来研究应聚焦于技术创新与跨学科融合，以强化金融机构抗风险能力。

本文旨在全面探究大数据时代背景下，金融工程技术怎样融入市场风险动态监控与预警机制之中，并形成相应的理论架构，先是对大数据和金融工程的基本原理展开梳理，之后又细致剖析了动态监控系统的技术框架，牵涉到的数据采集、即时处理以及分布式存储等关键环节均被涵盖其中，重点针对依靠机器学习和深度学习的风险预估模型改良及创建办法予以探讨，在此过程中详尽地论述了对监测系统达成过程中的指标体系规划、可视化开发以及合并检测等内容，最后经由对系统性能状况、稳定程度及其所存在技术缺陷的评价来展望其未来走向，从而给构建起高效且准确的市场风险防控体系给予强有力的理论根基和技术途径。

在经济高质量发展背景下，融资担保机构作为连接金融体系与实体经济的重要桥梁，在缓解中小微企业融资约束方面发挥着关键作用。然而，随着业务规模的持续扩张与宏观环境的不确定性增强，融资担保机构面临的信用风险与流动性风险不断积累，风险管理与业务发展之间的矛盾日益凸显。基于此，本文从融资担保风险管理与服务实体经济的协同关系出发，系统分析当前存在的主要问题，并结合业务实践提出优化路径。研究表明，应通过构建全流程风险管理体系、推动业务模式创新、强化资源整合能力及数字化赋能，实现风险防控与实体经济服务的动态均衡。

我国市场经济不断深化，供应链金融快速发展，应收账款保理行业作为解决中小企业融资难题、盘活企业流动资产的重要金融工具，迎来前所未有的发展机遇。不过在业务高速扩张背后，复杂交易结构和多变市场环境让其面临严峻财务管理风险。本文先系统性识别该行业经营中面临的主要财务风险，像信用、操作、流动性等风险，还深入剖析各类风险具体表现。接着借鉴国际权威COSO内部控制框架，从五个维度提出科学系统可操作的保理企业内部控制体系构建方案，研究为企业防范风险、提升竞争力和实现可持续发展提供理论与实践参考。

VIX指数作为全球金融市场衡量投资者情绪与预期波动的代表性指标，自问世以来在多次重大事件中展现出显著的市场反应能力。本文基于理论梳理与历史案例分析，系统探讨了VIX的构成原理、情绪映射机制及其在金融危机中的表现特征，进一步阐释其在资产配置与风险管理中的现实价值。同时指出，VIX虽具重要参考意义，但在适用性、反应时效及区域局限性方面亦存在一定约束。研究认为，VIX应作为市场风险识别与预警体系中的辅助工具，结合多维指标加以理性解读，方能更好服务于投资决策与市场稳定。

电费收入作为电力系统运营的核心资金流，其预测精度与风险防控能力直接影响能源经济安全。针对传统方法在预测偏差、波动辨识及跨部门协同等关键环节的局限性，本研究构建融合多源数据与动态分析技术的管理框架。通过建立数据治理体系实现异构信息整合，开发多维预警模型捕捉资金流动的时空异质性，并设计智能处置机制优化风险响应路径。该体系以差异化建模方法解析工商业与居民用户的缴费行为规律，依托动态优化机制实现风险传导的实时追踪，旨在为电力市场化改革中的资金管理提供兼具理论创新性与实践指导性的方法论支撑。

本文研究采用TVP-VAR-DY 模型，分析2021至2024年间中国碳交易市场和能源市场间的风险溢出效应及其方向，揭示两个市场之间的风险传递机制。研究发现，全国碳交易市场和能源市场之间存在随时间变化的双向非对称溢出效应，极端风险事件期间市场间的波动传递效应更加显著。从长期角度来看，碳市场通常充当风险的主要接收方，而焦炭市场则成为风险的主要发出者，原油市场则更多地表现为风险的接收端。最后，针对中国碳市场的构建、金融监管、风险防控和投资者策略等方面提出建议。

在数字技术驱动金融变革的浪潮中，金融科技已成为重塑行业生态的核心力量。2024年中国金融机构科技投入规模高达3949.6亿元，其中银行业占比73.13%（约2888.3亿元），足见金融科技在行业战略布局中的关键地位。为推动金融行业高质量发展，监管部门相继出台《金融科技发展规划(2022—2025年)》及《关于银行业保险业数字化转型的指导意见》，明确提出深化金融供给侧结构性改革，要求商业银行搭建数字化风控体系，提升投资交易效率，强化风险监测预警智能化水平，推动金融服务与风险管理的协同创新。

随着金融科技的蓬勃发展，大数据、人工智能、区块链等技术深度融入金融领域，在提升金融服务效率与创新能力的同时，也带来了前所未有的信息安全风险。本文聚焦金融科技发展背景，深入剖析信息安全风险的类型与特征，运用多种分析方法实现精准的风险识别，并构建全面系统的防控机制。研究表明，通过建立科学的风险识别体系与完善的防控机制，能够有效降低信息安全风险，保障金融科技稳健发展。

当代社会，已进入了全球化工业建设革命浪潮，在该背景下，金融科技逐渐出现在人们视野，并成为了全球各国的研究热点。金融科技的开发和应用在推动社会发展进步的同时，也带来了一些风险，为了能够充分发挥金融科技作用优势，就需要相关领域关注金融科技应用风险防控。文中便以此为核心展开研究，探讨金融科技应用风险的基本特点，结合实际情况分析金融科技应用存在的主要风险，并提出有效的风险防控建议，以期为行业实践提供参考。

袁修齐，肖萍* 湖南人文科技学院数学与金融学院，娄底市，湖南省，中国 摘要：为了考察供应链金融下科技型中小企业的信用风险，并对该信用风险进行合理分析，本文运用Logistic回归模型构建了信用风险评估体系。预估供应链金融下科技型中小企业的信用情况，并对该企业的风险做出管控分析，最后提出有效的策略。本论文通过东方财富以及搜狐证券等调取了供应链金融下55个科技型中小企业的相关数据，构建Logistic回归模型，评估上述企业的信用风险，并在此基础上做出风险评估，并给出管控分析。研究表明企业的信用风险有着许多的决定因素，其中企业的偿债能力、盈利能力、基本情况、营运能力以及成长能力等对企业的信用风险影响最大。企业应当实时观测自身的资产负债率、收益、资金运转能力、现金流比率等对自身信用风险进行监控。

保险行业快速发展，随着人工智能、区块链、云计算、大数据等金融科技赋能保险业，带来了互联网保险的快速崛起、保险科技的高质量发展、保险数字化的转型赋能等更加深远的影响。但同时金融科技也对保险业的金融管理带来了更大的挑战，比如数据安全和隐私泄露风险、技术挑战、监管环境更加复杂和科技复合型人才短缺等。因此研究金融科技对保险业金融管理的影响，并基于金融科技对保险业金融管理提出建议，对保险行业以后的发展具有重要意义。

随着全球化进程加速与数字经济蓬勃发展，数据跨境流动已成为金融科技行业常态。在此背景下，金融科技信息安全面临跨境数据泄露、监管规则冲突等复杂风险，传统单一的监管模式难以应对。本文深入剖析数据跨境流动对金融科技信息安全监管带来的挑战，探讨构建高效的监管协同机制，涵盖国内跨部门协同、国际间监管合作等方面。

在数字技术革命背景下，银行表外业务面临技术滞后与需求升级的双重挑战。本文基于“技术-需求”双重视角，构建“技术赋能→业务模式重构→需求牵引→创新深化”闭环框架，揭示金融科技通过技术工具降低表外业务成本、提升风险定价精度、扩展服务场景，推动业务从“传统标准化”向“数字化精准化”转型；同时，企业全链条风险管理、个人个性化配置等需求升级，引导银行创新结构化产品与智能服务，并倒逼技术适配。研究表明，技术赋能与需求牵引通过动态匹配形成正向循环，协同提升表外业务效率与附加值，为数字经济时代银行表外业务可持续发展提供理论与实践支撑。

随着互联网普及与金融数字化转型,金融欺诈呈现数字化、专业化、系统化特征,对金融行业秩序与社会稳定造成严重威胁。本文基于问卷的方式获取数据,用BP神经网络、结构方程模型分析。神经网络明确各因素影响权重,结构方程验证多变量与欺诈的路径关联,最后从国家机构、社会、个人层面提监管、教育、自我防护等防范建议,为应对金融欺诈提供参考。

金融数字化转型的加速催生了多机构协同风控的迫切需求，但数据主权的法律约束与隐私泄露风险严重制约了跨机构数据共享的可行性。在《数据安全法》与《个人信息保护法》框架下，金融机构需在风险联防联控与数据资产保护间寻求平衡。传统联合建模方法因依赖明文数据交互或中间参数传输，难以规避隐私泄露与主权争议，而全流程密文计算技术成为破局关键。本文聚焦于上述问题，提出一种基于同态加密的多机构协同风控框架，并引入动态博弈模型解析数据主权博弈的均衡路径。

余鳳全 上海财经大学金融学院，上海 摘要：在不确定性高度存在且交易成本显著的现实情境中，传统保险市场结构正面临制度性挑战。本文基于科斯定理的制度边界逻辑，探讨保险科技如何通过压缩信息成本、优化风险治理机制与重构商业模式，有效提升风险的可保性。文章分析保险价值链与信息管理六层架构中的技术介入，强调其制度功能不止于运营效率，更在于改善信息分布与资源配置。通过“大数法则”中的数量与质量双维度，本文指出风险信息可得性的提升可重塑风险转移曲线，将促使保险、再保险与资本市场结构趋于融合。在平台化与结构创新的趋势下，P2P保险、参数型合约与链上治理等新兴机制挑战既有市场角色划分。最终指出，保险科技不仅是技术革新，更是制度演进的关键动力，有助于扩大保险市场边界，提升公平性与风险保障能力。

我国已成为全球第二大财富管理市场,股票、债券市场规模稳居世界第二,进一步加强金融投资市场风险机制建设,对于维护国家金融安全、促进经济高质量发展、维护投资者合法权益等方面都具有重要意义。基于此,本文从风险机制视角出发,论述了金融市场投资风险机制相关概念、理论和主要应用,分析了我国实际金融市场投资风险管理机制发展现状,指明了存在的风险机制的相对滞后性、跨境和联合监管不足、金融科技风险持续增加、风控意识淡薄等问题与不足,并从宏观、中观、微观三个层面出发,探讨了进一步加强金融市场投资风险机制建设的对策路径。

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With the rapid development of the digital economy, supply chain finance, as an important financial model for serving the real economy and alleviating financing constraints faced by small and medium-sized enterprises (SMEs), has become a key tool for promoting industrial chain collaboration and optimizing resource allocation. However, in practice, supply chain finance still faces challenges such as difficulties in verifying the authenticity of trade backgrounds, instability in credit transmission from core enterprises, pronounced information asymmetry, and insufficient efficiency in multi-stakeholder coordination. These issues lead to the intertwined accumulation of credit risk, operational risk, and compliance risk, thereby constraining the sustainable development of supply chain finance. Blockchain technology, characterized by distributed ledgers, immutability, traceability, and smart contracts, provides a new technological foundation and institutional support for risk governance in supply chain finance. Based on the logic of risk generation in supply chain finance, this paper systematically reviews major risk types and risk transmission mechanisms, and constructs an analytical framework for blockchain-enabled risk governance. It examines how blockchain influences risk identification, risk prevention, and risk accountability from four dimensions: information credibility, process transparency, credit constraints, and regulatory coordination. The study argues that blockchain not only reduces information asymmetry and moral hazard in supply chain finance transactions, but also facilitates the formation of a collaborative governance system, enabling a shift in risk governance from ex post remediation to ex ante early warning and process-based control. To enhance the practical effectiveness of blockchain-enabled risk governance in supply chain finance, this paper further proposes policy recommendations including promoting standardized data-sharing mechanisms, improving smart contract rule systems, strengthening credit constraints on core enterprises, and establishing a coordinated regulatory technology framework, with the aim of providing theoretical support and practical reference for the high-quality development of supply chain finance in China.

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In this paper, we propose and demonstrate a prototype for leveraging Generative AI (GenAI) in financial risk analysis, specifically focusing on fine-tuning GPT models with proprietary data. Financial risk modeling, development, validation, and approval require not only advanced AI techniques but also careful implementation, given the vast and complex datasets involved in such tasks. The research underscores the critical importance of human oversight in mitigating potential failures that can arise from fully automated mathematical models. The study explores the application of Large Language Models (LLMs) in various financial risk domains, such as credit risk assessment, market risk forecasting, and anomaly detection. While synthetic data generation is excluded from this work, the research highlights the use of zero-shot classification leveraging Hugging Face models and OpenAI tools. ChatGPT achieved over 70% accuracy in generating relevant questions and demonstrated 60% correctness in classification tasks. Additionally, we present a prototype pipeline that integrates GenAI capabilities into financial workflows, which is implementable on small-scale computing systems. This includes backend testing via Flask and rapid prototyping using cURL commands, offering a practical approach to testing and deploying models. By fine-tuning GenAI with domain-specific data and optimizing decision-making processes, this research highlights the transformative potential of integrating generative AI into financial risk management. The study provides insights into enhancing model efficiency, regulatory compliance, and scalability. Moreover, it addresses critical challenges such as handling large datasets and ensuring ethical AI use in decision-making systems. This work contributes to advancing the adoption of GenAI in financial analytics, paving the way for innovative, robust, and efficient methodologies to support the evolving demands of the financial sector.

This paper presents a comprehensive review of the latest development in Generative AI and Big Data with application in Finance. 2025 is the year of Agentic AI, marking a pivotal shift in generative AI (Gen AI) and its integration with big data. This paper explores the synergies between Gen AI and big data, particularly in financial risk management, proposing strategies for deeper integration. By citing the latest white papers and focusing on developments over the past two years, we expand the scope of research in this transformative domain. Recent advancements in Gen AI, such as GPT-4, VAE-GANs, and explainable AI architectures, demonstrate significant improvements, including 25–40% gains in workflow efficiency and 18–30% reductions in error margins for financial and enterprise systems (based on current literature). We segment these developments to identify potential applications in market and credit risk, while addressing challenges such as the lack of universal Python full-stack architectures. To bridge this gap, we propose recommendations for seamless communication between Gen AI and big data systems, enabling scalable solutions and actionable insights. This paper underscores the role of Gen AI in transforming big data into operational excellence, setting the stage for future innovations in financial and enterprise analytics.

Risk management is on the rise every day in the banking industry due to the rising uncertain economic climate. Despite the growing role of banks in the country's growth, the governing bodies of these institutions face numerous risks. Risk management is perceived to be the secret to the success and failure of any financial institution. The study explores the effect of financial risk on the performance of listed banks in Bahrain and the relative value of the most popular types of risk. The research covers 11 of the 18 banks in Bahrain from 2014 to 2018. Based on the availability of data, data was collected from the Bahrain Stock Exchange Database. Alternatively, the most common metrics, ROA, was used for bank performance and risk measures. Four forms of financial risk have been used, namely capital risk, exchange rate risk, liquidity risk and operating risk. Regression analysis reveals that there is an insignificant relationship between bank performance, exchange rate risk, liquidity risk and operating risk. The findings also indicate an important positive relationship between bank performance and capital risk. In addition, the findings indicate that capital risk is the most significant form of risk. The research advises that attention be paid to operational risk, which is primarily related to uncertainty about the earnings of a financial company due to computer system failures, mistakes, abuse by staff, or risk of loss due to unforeseen operating expenses.

Enterprise financial risk prediction is a critical component in ensuring economic stability and business sustainability. The intelligent early warning model enhances decision-making by detecting potential risks before they escalate into significant financial losses. However, traditional models often rely on static indicators and linear assumptions, lacking the capacity to capture complex, temporal patterns in enterprise financial data. This limitation reduces their effectiveness in dynamic business environments. To address these challenges, this paper proposes a Bidirectional Long Short-Term Memory-Based Financial Risk Prediction (BiLSTM-FRP) model with an Attention Mechanism. The model leverages bidirectional long short-term memory networks to understand both forward and backward financial trends, while the attention layer prioritizes the most influential features that affect enterprise risk. This hybrid deep learning approach enables the system to generate real-time risk scores and trigger early warnings, allowing stakeholders to implement proactive strategies. Experimental results demonstrate that the proposed method significantly outperforms existing baseline models in accuracy, recall, and early detection performance. This confirms its potential as a reliable tool for financial institutions and enterprises in mitigating risk and enhancing financial resilience. The BiLSTM-FRP model outperformed four financial risk prediction variables, achieving high accuracy (0.93), strong recall (0.91), and effective early detection (0.90). Its financial resilience score (0.88) indicates robustness in volatile markets, highlighting its reliability and potential as a real-time financial risk management and prediction tool.

Introduction The intersection of big data analytics and financial risk management has spurred significant methodological innovation and organizational change. Despite growing research activity, the literature remains fragmented, with notable gaps in comparative effectiveness, cross-sectoral applicability, and the use of non-traditional data sources. Methods Following the PRISMA 2020 protocol, a systematic review was conducted on 21 peer-reviewed studies published between 2016 and June 2025. The review evaluated the methodological diversity and effectiveness of machine learning and hybrid approaches in financial risk management. Results The analysis mapped the relative strengths and limitations of neural networks, ensemble learning, fuzzy logic, and hybrid optimization across credit, fraud, systemic, and operational risk. Advanced machine learning techniques consistently demonstrated strong predictive accuracy, yet real-world deployment remained geographically concentrated, primarily in Chinese and European banking and fintech sectors. Applications involving alternative and unstructured data, such as IoT signals and behavioral analytics, were largely experimental and faced both technical and governance challenges. Discussion/conclusion The findings underscore the scarcity of systematic benchmarking across risk types and organizational contexts, as well as the limited attention to explainability in current implementations. This review identifies an urgent need for comparative, cross-jurisdictional studies, stronger field validation, and open science practices to bridge the gap between technical advances and their operational impact in big data–enabled financial risk management.

This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel at capturing long-term dependencies in financial data, leading to more robust predictions and improved portfolio outcomes. Feature selection and preprocessing were crucial in maximizing model performance. Portfolio optimization using machine learning models, when combined with traditional optimization techniques, resulted in superior Sharpe and Sortino ratios. These findings highlight the potential of machine learning to enhance real-time financial decision-making, offering more adaptive and resilient strategies for managing investment portfolios in dynamic market environments. This research provides valuable insights into the integration of machine learning for financial risk prediction and portfolio management, with implications for future advancements in the field.

This paper proposes a financial risk identification framework based on the Knowledge-enhanced Neural Network (KAN) to address the challenges of highly concealed risk behaviors, complex data structures, and sparse anomalies in the digital financial environment. The method is designed around function-level prior expression and structure-aware mechanisms. It introduces a differentiable basis function encoder to perform structured mapping of multimodal transaction data. A structure-aware transformation stack is then used to extract high-order behavioral interaction patterns. A high-order composite function module is applied to achieve the deep expression of risk factors. To enhance the stability of risk discrimination in dynamic environments, a contrastive consistency regularizer is further designed to guide the model in maintaining semantic alignment and structural robustness across multimodal representations. The proposed method demonstrates significant advantages in risk identification accuracy and robustness across several key performance metrics. It effectively alleviates the limitations of traditional neural models in heterogeneous financial scenarios, especially their weak generalization and delayed response to low-intensity risk signals. Through systematic model design and comprehensive metric evaluation, this work validates the effectiveness of KAN in enhancing risk pattern characterization across structural representation, layered composition, and contrastive reasoning. It provides both theoretical support and algorithmic foundations for building interpretable, controllable, and high-performance intelligent financial risk identification systems.

The purpose of this study is to explore the impact of credit risk, liquidity risk, and operational risk on the profitability of banks in Jordan. Using data from conventional banks between 2017 and 2023, the study employs the generalized method of moments (GMM) for coefficient estimation to account for endogenous variables. The findings indicate that both credit and liquidity risks negatively affect bank profitability, with this negative effect evident when the risks are considered individually or together. However, operational risk does not have a significant direct impact on financial performance. This research contributes to the understanding of how financial risks influence bank performance, particularly in developing economies, and provides practical insights for senior management in financial institutions, offering guidance on risk management strategies to mitigate the adverse effects of credit and liquidity risks.

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This study aims to examine the impact of environmental, social, and governance (ESG) practices on financial risk, with operational ability and profitability as moderating variables, within the infrastructure sector in Indonesia. The sample consists of 14 infrastructure companies listed on the Indonesia Stock Exchange over the period of 2020–2022, resulting in a total of 42 firm-year observations. The study employs a random-effects model for panel data regression analysis to test the hypotheses. ESG serves as the independent variable, financial risk as the dependent variable, and operational ability (measured by total asset turnover) and profitability (measured by return on assets) as moderating variables. The results reveal that ESG practices have a significant positive impact on financial risk. Furthermore, operational ability negatively moderates the relationship between ESG and financial risk, while profitability positively moderates this relationship. These findings suggest that although higher ESG scores may elevate financial risk due to associated costs and uncertainties, firms with greater operational efficiency and profitability are better equipped to mitigate and manage these risks more effectively. This study focuses on infrastructure companies in Indonesia, which may limit the generalizability of the findings to other sectors or geographic regions. Future research could broaden the scope by incorporating diverse industries and cross-country contexts to enhance the robustness and applicability of the results. The findings offer valuable insights for policymakers and corporate managers. Policymakers can develop frameworks to encourage sustainable practices without imposing excessive financial burdens on companies. Managers can leverage operational efficiency and profitability to integrate ESG practices effectively, thereby balancing sustainability goals with financial performance. This study contributes to the scarce literature on the moderating effects of operational ability and profitability on the relationship between ESG practices and financial risk, particularly in the context of the infrastructure sector in Indonesia.

ABSTRACT Balancing financing and investment periods presents a key challenge for companies to manage capital use and financial risk. Recently, maturity mismatches have caused significant financial difficulties for enterprises. Consequently, understanding how maturity mismatch affects financial risk has become a major societal concern. Using data from Chinese listed companies in Shanghai and Shenzhen A-shares from 2008 to 2022, this study explores how maturity mismatch between investment and financing affects corporate financial risk through financial flexibility. Results indicate that maturity mismatch significantly promotes corporate financial risk, primarily attributed to reduced financial flexibility. These results hold, even after conducting several robustness checks. This relationship is more pronounced among CEOs with a financial background and stable institutional investors. This study offers guidelines for financial risk management in developing countries, suggesting that enterprises should optimize investment and financing allocations, emphasize financial flexibility, hire CEOs with financial expertise, and attract stable institutional investors.

This study presents a systematic review and bibliometric analysis of the relationship between sustainability practices—commonly framed within the environmental, social, and governance (ESG) framework—and both corporate value creation and financial risk mitigation. Our primary objective is to assess how ESG initiatives affect firm outcomes, with particular emphasis on risk reduction, a dimension less explored in the economic and financial literature. The search was conducted in the Web of Science database on 15 June 2024, using the keywords “ESG and Financial Risk” and “ESG and Valuation,” yielding 1074 initial records. After applying inclusion and exclusion criteria, we analyzed the final sample through descriptive and frequency-based methods. Findings reveal no clear consensus on the connection between ESG and value creation, with results varying across sectors, firm sizes, regions, and specific ESG components. In contrast, the evidence supporting the link between ESG practices and financial risk mitigation is stronger: 68% of the reviewed studies reported a positive relationship, while only 5% found negative effects. This review underscores the potential of sustainability as a risk-management mechanism and highlights research gaps that warrant deeper exploration. Limitations include heterogeneity of methodologies, metrics, and contexts among the studies reviewed.

Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates the contagion path from individual liquidity crisis to systemic repayment crisis. In order to test whether bank financial technology (hereinafter referred to as “BankFintech”) can mitigate SDLU and reduce the possibility of financial risks, this study matched the loan data of China’s A-share listed companies with the patent data of bank-invented Fintech from 2013 to 2022 to construct the BankFintech Development Index for empirical analysis. The empirical results show that the development of BankFintech can significantly inhibit SDLU. The mechanism test reveals that BankFintech reduces bank credit risk and liquidity risk by lowering firms’ risk-weighted assets, improving capital adequacy and liquidity ratios, tilts banks’ lending preferences toward duration-matched long-term financing, and “forces” enterprises to take the initiative to improve their financial health and information transparency, enhance their ability to obtain long-term loans, and realize the active management of mismatch risk. Heterogeneity analysis finds that the effect is more significant in non-state-owned enterprises and technology-intensive industries. Further analysis shows that the level of enterprise digitization, the intensity of financial regulation, and related financial policies significantly moderate the marginal effect between the two. This study verified the “Porter’s Risk Mitigation Hypothesis” of Fintech, providing empirical evidence for effectively cracking the financial vulnerability caused by debt maturity mismatch and deepening financial supply-side reform.

This study investigates the influence of financial risk management strategies on organizational stability, aiming to understand how diverse risk mitigation practices contribute to sustaining firm performance amid economic uncertainties. Employing a systematic literature review methodology, the study analyzes peer-reviewed articles, institutional reports, and empirical research published between 2020 and 2025. Data sources were rigorously selected using defined inclusion and exclusion criteria, and findings were synthesized through a content analysis approach. The review identifies key financial risk management strategies such as hedging, diversification, enterprise risk management frameworks, and the integration of corporate governance and regulatory compliance as critical to enhancing organizational resilience. Technological innovations, including artificial intelligence and blockchain, are found to significantly improve risk identification, monitoring, and mitigation processes. The study also highlights challenges in implementing risk management frameworks, including cultural resistance, resource constraints, and regulatory complexities. Implications for practitioners emphasize the need for dynamic, technology-enabled, and culturally embedded risk management approaches, while policymakers are encouraged to foster regulatory environments that balance oversight with innovation. Limitations of the study include potential publication bias and the rapid evolution of risk technologies not fully captured in the literature. Future research directions call for sector-specific analyses, longitudinal studies on technological impacts, and deeper exploration of sustainability-related risks. The findings underscore the imperative for organizations to adopt integrated, adaptive financial risk management practices to ensure long-term stability and competitive advantage in an increasingly volatile global economy. Keywords: Financial Risk Management, Organizational Stability, Enterprise Risk Management, Corporate Governance.

Policymakers, governments, and Islamic financial institutions are increasingly focusing on sustainable development, leading to an in-depth examination of current sustainable finance practices, projects, and product portfolios. This study examines the role of Islamic sustainable finance (ISF) in promoting Sustainable Development Goals (SDGs) to avert financial risk in the management of Islamic Finance Products (ISFP). Through qualitative analysis, the study conducts a critical literature review (CLR) that incorporates conceptual, theoretical, and empirical perspectives on ISF and SDGs and addresses two specific research questions. Our study examines over 48 journals from 2010 to 2024 and provides insights into how ISF advances the SDGs across all environmental, social, and economic dimensions. It also highlights that ISF promotes green entrepreneurship by investing in sustainable projects, supporting SMEs, and offering alternative financing. ISF also promotes financial stability, justice, and growth and is consistent with the principles of Maqasid al-Shari’ah. Key ISF mechanisms that promote the SDGs include Islamic Green Sukuk, Socially Responsible Investment Funds, Islamic Microfinance, and Islamic Impact Investing. Integrating Islamic ethical principles into financial activities is crucial for inclusive and sustainable economic development. These qualitative insights are critical for policymakers, Islamic financial institutions, Halal entrepreneurs, environmentalists, and investors to understand the potential of Islamic social finance (ISF) to support sustainable practices, projects, and portfolios. Furthermore, the ISFs alignment with Maqasid al-Shari’ah highlights its importance in promoting sustainable development while mitigating financial risk in ISFPs management. The study offers robust contributions to the existing literature to provide comprehensive insights into how ISF can be effectively used to promote SDGs.

Abstract This paper examines the transformative role of Artificial Intelligence (AI) in enhancing financial risk management practices within the public sector in different context. With the increasing complexity of financial markets and the growing demand for transparency and efficiency in public finance management, traditional methods of risk identification and mitigation are proving to be inadequate. AI and machine learning technologies offer a promising alternative, with their ability to process vast amounts of data, recognize patterns, and predict future trends with a high degree of accuracy. We delve into the application of predictive models for identifying various financial risks, including credit, market, liquidity, and operational risks, and explore how AI algorithms can optimize decision-making processes through scenario analysis, resource allocation, and policy impact analysis. The paper also addresses the challenges associated with integrating AI into public financial management systems, such as data privacy concerns, the need for skilled personnel, and the importance of developing regulatory frameworks that ensure ethical use and transparency. By presenting case studies and real-world applications, this study highlights both the successes and failures of AI implementations in the public sector, providing valuable lessons learned and best practices. As AI technologies continue to evolve, this paper underscores the need for ongoing research and adaptation to fully realize their potential in making public sector financial risk management more proactive, efficient, and resilient.

This research explores the integration of artificial intelligence (AI) and the Internet of Things (IoT) within corporate social responsibility (CSR) strategies, focusing on financial risk management and sustainable development. Employing a novel Coevolutionary multi-paradigm approach to technological development, this study examines how these technologies can be embedded into CSR practices to enhance sustainability and manage risks effectively. The findings reveal that successful integration depends significantly on the adaptability of institutional structures to support technological innovations. This study contributes to the literature by providing a comprehensive analysis of the intersection of AI, IoT, and CSR, highlighting the necessity for robust mechanisms and policies that ensure security, standardization, and sustainable use of emerging technologies. Through this investigation, this research offers a new perspective on leveraging advanced technologies to advance corporate sustainability and risk management objectives.

Small and medium-sized enterprises (SMEs) contribute significantly to the economic growth of emerging economies. However, SMEs have difficulty maintaining their performance. This paper examines the impact of financial literacy on the sustainability of SMEs with the roles of access to finance, financial risk attitude, and organizational risk-taking tolerance as mediators. This study concentrated on the Indonesian market and used questionnaires to collect data from the owners or chief financial officers of SMEs. The extent of the correlations was examined using partial least squares structural equation modeling. It was found that financial literacy, financial risk attitude, and organizational risk-taking tolerance are significant antecedents of SME sustainability. Access to finance, on the other hand, was not a factor in SME sustainability. A financial risk mentality was also revealed to have a substantial influence on the association between financial literacy and SME viability in the study. Furthermore, the relationship between financial literacy and SME viability was found to be mediated by organizational risk-taking tolerance. This study has significant relevance for SMEs, as it confirms that financial literacy is crucial for continued success. SMEs with proper financial literacy will have a superior attitude to financial risk and organizational risk-taking tolerance, ensuring their sustainability. Future studies can investigate SME sustainability in other emerging economies and compare their performance and sustainability.

In today’s dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that seamlessly merges deep autoencoder (DAE) models with reinforcement learning (RL) techniques to enhance financial risk forecasting within the realm of supply chain management. The primary objective of this research is to optimize financial decision-making processes by extracting key feature representations from financial data and leveraging RL for decision optimization. To achieve this, the paper presents the PSO-SDAE model, a novel and sophisticated approach to financial risk forecasting. By incorporating advanced noise reduction features and optimization algorithms, the PSO-SDAE model significantly enhances the accuracy and reliability of financial risk predictions. Notably, the PSO-SDAE model goes beyond traditional forecasting methods by addressing the need for real-time decision-making in the rapidly evolving landscape of financial risk management. This is achieved through the utilization of a distributed RL algorithm, which expedites the processing of supply chain data while maintaining both efficiency and accuracy. The results of our study showcase the exceptional precision of the PSO-SDAE model in predicting financial risks, underscoring its efficacy for proactive risk management within supply chain operations. Moreover, the augmented processing speed of the model enables real-time analysis and decision-making — a critical capability in today’s fast-paced business environment.

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With the accelerated development of economic globalization, it is of great significance to strengthen the ability to measure, evaluate, and warn of systemic financial risks for preventing and defusing financial risks. Thus, this research established the Time-Varying Parameter Factor-Augmented Vector Autoregression model (TVP-FAVAR), combined with the Markov Regime Switching Autoregressive Model, to dynamically measure China’s systemic financial risk. The network public opinion index is constructed and introduced into the financial risk early warning system to capture the dynamic impact of market sentiment on financial risks. After testing the nonlinear causal relationship between financial indicators based on the transfer entropy method, the Transformer deep learning model is applied to build a financial risk early warning system, and the performance is compared to traditional methods. The experimental results showed that (1) the trend of the systemic financial risk index based on the dynamic measurement of the TVP-FAVAR model fitted the actual situation well and that (2) the Transformer model public opinion index could fully and effectively mine the nonlinear relationship between data. Compared to traditional machine learning methods, the Transformer model has significant advantages in stronger prediction accuracy and generalization ability. This study provided a new technical path for financial risk early warning and has important reference value for improving the financial regulatory system.

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ABSTRACT This study introduces a novel methodology for measuring a monthly FinTech index to address the timeliness challenges in systemic financial risk prediction using FinTech data. By employing machine learning models, the SHAP algorithm, and partial dependence plots, we assess the role of FinTech of different technical types as drivers of systemic financial risk. We simultaneously conduct comparative analyses of the predictive performance among traditional AR models, machine learning models, and ensemble forecasting models. The findings indicate that artificial intelligence, big data, and internet FinTech have the most significant influence on systemic financial risk, whereas IoT, cloud computing, and blockchain FinTech have a relatively limited impact. Regarding predictive performance, machine learning models generally outperform traditional AR models, with the K-Nearest Neighbors (KNN) model demonstrating particularly superior prediction accuracy and optimal robustness. Furthermore, three ensemble forecasting models (MEAN, MEDIAN, and TRIMMED MEAN) outperform individual machine learning models in terms of predictive performance. Notably, the impact of FinTech of different technical types on systemic financial risk exhibits diversified and nonlinear relationships, with significant variations in directional effects, sensitivity, and inflection points.

In an era that is characterized by digitalization and climate-sensitivities, systemic financial risks have become increasingly complex. They threaten the stability of the global market, which is interconnected. This study aims to address a crucial gap in existing literature by reviewing historical and emerging mechanisms of risk propagation alongside other preventive tools. The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 methodology to shortlist 46 peer-reviewed open-access articles published between 2021–2025. These articles were sourced from ScienceDirect and categorized into three thematic areas: historical propagation, emerging risks, and innovative prevention mechanisms. The review confirms that traditional drivers such as bank failures and interbank contagion remain relevant, as put forth by Andrieş et al. (2022). Additionally, it establishes that modern threats include disruptions caused by to big financial system, climate-transition-shocks, and crypto-financial instability (Crandall, 2025). Hence, technological interventions like artificial intelligence (AI)-based stress testing, blockchain-based transparency, and environmental, social, governance (ESG)-driven risk mitigations are reshaping the financial risk landscape. However, they face regulatory and governance barriers. Thus, the findings of this study primarily emphasize the need to globally harmonize policy frameworks that integrate sustainability, real-time monitoring, and digital innovation. This study entails an integrated, forward-looking synthesis for policymakers who seek resilient financial architectures in an era of technological transformation and environmental uncertainty.

Institutional investors exert significant influence on the operations and development of financial institutions, with different categories of investors playing distinct roles. We contend that institutional investor diversity may affect systemic financial risk. This study proposes novel measures of institutional investor diversity across 84 China’s financial institutions and employs Extreme Value Theory (EVT) to estimate systemic financial risk. Based on this, we empirically examine the relationship and underlying mechanisms. Baseline regression indicates that greater institutional investor diversity plays an effective role in controlling systemic financial risk. We further find that institutional investor diversity significantly suppresses herding behavior, thereby indirectly reducing systemic risk. Moreover, this effect is more pronounced in financial institutions operating in more developed market environments, under stronger external supervision, and with higher levels of technological advancement, as well as in securities firms. These findings not only contribute to the literature on the economic impact of institutional investors but also provide valuable insights for strengthening systemic financial risk control.

This paper aims to investigate how digital inclusive finance affects systemic financial risk through spatial modeling. We analyze regional variations and mechanism pathways to understand their risk governance implications in the digital era. We develop a spatial game model for theoretical analysis of digital inclusive finance’s risk mitigation mechanisms. Empirically, we construct a city-level panel data model using China’s digital inclusive finance index and systemic financial risk index to test these relationships quantitatively. Digital inclusive finance fundamentally mitigates systemic risk by enhancing resource allocation efficiency – a mechanism particularly potent under strong financial regulation. Empirically, China’s city-level data confirm a significant risk mitigation effect, with central/western regions showing greater effects than eastern areas. Coverage breadth delivers stronger mitigation than usage depth, demonstrating how implementation dimensions shape real-world outcomes. Policymakers should develop tailored regional strategies: prioritize digital inclusive finance’s coverage expansion in underdeveloped regions to bridge financial service gaps while focusing on its usage deepening in mature markets. Crucially, all regions require strengthened prudential regulation to maximize stability benefits through digital-regulatory synergy. This study offers implementable frameworks for digital-era financial governance in emerging economies. Using spatial game modeling, it validates digital inclusive finance’s systemic risk mitigation and quantifies coverage breadth’s superiority over usage depth, revealing regional disparities. The work uncovers the regulatory intensity’s threshold effect, exploring digital-regulatory synergy dynamics.

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The stability of the financial system is a worldwide concern that is crucial for economic well-being. Corporate risk perceptions reflecting firms’ assessments of potential threats are integral to understanding financial interconnectedness and systemic risk, yet this potential has not been uncovered. This study employs text mining of risk factor disclosures to extract the risks perceived by financial institutions and measure the homogeneity of their perceptions. Our findings indicate that post-crisis, risk perceptions among firms become more homogeneous, driven by heightened awareness of shared risks. Differences exist in intra-sector risk perception homogeneity across sectors, whereas a consistent rise in inter-sector homogeneity is observed. Greater perception homogeneity among financial institutions, as indicated by their network positions, is positively associated with higher systemic risk, irrespective of whether the risks are general or specific. However, the risk perception homogeneity can help mitigate systemic risk once a financial crisis occurs.

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This study centers on the pivotal issue of systemic financial risk forecasting by employing ensemble classification algorithms to construct a warning model. The research delves into the application of ensemble learning methods such as Bagging, Boosting, and Random Forests in risk prediction. By curating samples and constructing a comprehensive dataset, this paper establishes a robust system for measuring systemic financial risk. In the empirical analysis, the optimized ensemble classification algorithms were applied to risk warning experiments within the Chinese financial market. The results indicate that the model demonstrates significant advantages in identifying potential systemic risks, with superior accuracy and stability compared to traditional single classification methods. The study also identifies macroeconomic indicators, financial market volatility, and cross-market correlations as key factors influencing systemic risk. The innovation of this paper lies in integrating advanced machine learning techniques with financial risk management theory, offering new insights and tools for regulatory authorities in formulating risk prevention policies. The findings hold substantial theoretical value and practical significance for enhancing the resilience of the financial system and maintaining financial stability.

Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

The new development pattern of the dual cycle is an important strategic deployment by the Chinese government in response to the complex and changing economic environment and a new stage of economic development. Based on the perspective of this new dual-cycle development pattern, this paper explores the time-varying nonlinear relationship between this new development pattern and systemic financial risk by constructing two models: the TVP-SVAR-SV and the MS-VAR. The empirical results show that, first, there was a significant negative impulse response effect between the new dual-cycle development pattern and systemic financial risk. The inhibitory effects of the domestic cycle and short-term pulse response were more significant than those of the international cycle and medium- and long-term pulse responses. In addition, the inhibitory effect of the new dual-cycle development pattern on systemic financial risk exerted different impact effects at different timepoints, with significant variability. Second, there was a significant nonlinear relationship between the new development pattern and systemic financial risk. The MSIH(2)-VAR(1) model identified two regional states: the financial risk release stage and the financial risk accumulation stage. This effect lasted longer in zone 2 (financial risk accumulation phase), with a sample size of more than 60 % of the total sample and a duration of 6.12. Third, in the impulse response analysis of different zones, a mutually reinforcing effect was found between the domestic and international cycles, and both exerted a significant suppressive effect on systemic financial risk, which was more significant in zone 1. The findings of this paper have important theoretical and practical significance for constructing and perfecting the new dual-cycle development pattern, promoting the high-quality development of China's financial market, and preventing the accumulation and occurrence of systemic financial risk.

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the “decomposition–reconstruction–integration” approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning.

This paper constructs a systematic financial risk index system by selecting indicators from five aspects: macroeconomic risk, stock market risk, financial institution risk, international market risk and real estate market risk, uses the entropy value method to determine the weights of the indicators and calculates the systematic financial risk index from 2007 to 2024. The results of the study show that the systemic financial risk index has a rising trend, especially in recent years. The decline in housing prices and the slowdown in macroeconomic growth result in a significant increase in systemic financial risk. This paper provides some reference for stabilizing house prices and reducing systemic financial risk.

Systemic financial risk is closely linked to national financial stability and economic security. The ultimate consequence of a systemic financial risk event is a systemic financial crisis, which can disrupt the entire financial market and even the broader economic system. Therefore, enhancing the monitoring and analytical capabilities for systemic financial risks is crucial for effective financial management. This paper employs macroeconomic and market indicators to construct a BP neural network early warning model for predicting systemic risk. First, it conducts a theoretical examination of systemic financial risks. Next, based on this theoretical framework, it develops a warning indicator system comprising 14 economic and financial indicators. Subsequently, principal component analysis is applied to optimize and refine the selection of systemic financial risk indicators, resulting in four representative common factors. Finally, an empirical analysis is conducted using the BP neural network early warning model. The model is trained with data from the first quarter of 2010 to the fourth quarter of 2022, while the first quarter of 2022 to the third quarter of 2023 serves as the validation set. The trained BP neural network model is then used to predict the validation set. The results indicate that the actual and predicted values of the validation set are generally consistent, with a standard error of 0.411. This suggests that the model can accurately predict systemic financial risks.

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Traditional industrial enterprises are primarily high pollution enterprises and high energy consumption. The achievement of the “dual carbon” motive in Chinese economic activities and environmental protection plays a chief role incorporating the environmental, social, and governance (ESG) into the risk management system of industrial enterprises. However, the industrial enterprises participating in ESG rating account for 46.97% of the listed enterprises, and the industrial enterprises that pay attention to ESG information disclosure account for 35.77% of the listed enterprises. Is ESG rating essential for the enhancement of industrial enterprises? Based on the transformation and upgrading of industrial enterprises, this research investigates how the ESG rating and ESG information disclosure affect the financial risk of industrial enterprises in China. The findings suggest that: (1) In China for industrial enterprise ESG plays an essential role which results in decrease in financial problems of industrial enterprise. (2) ESG is influential on monetary risk of industrial enterprise, the quality of enterprise ESG information disclosure plays an important intermediary role. (3) The lagging of ESG rating by one period is significantly positive related to the financial risk of industrial enterprises, and the financial risk prediction of industrial enterprises can be effectively indicated by the ESG rating data, which exhibits a stronger correlation coefficient than the existing rating system.

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 integration of digital technologies such as artificial intelligence (AI), blockchain, and big data analytics into financial risk management has substantially altered operational dynamics within various industries. This paper explores the dual-edged impact of these technologies, emphasizing both the opportunities they create and the challenges they present. Opportunities discussed include enhanced decision-making through advanced data processing, increased transactional transparency and security via blockchain, and improved operational efficiencies through automation. Conversely, the challenges encompass heightened cybersecurity risks, evolving regulatory compliance demands, costly technological integrations, and the emerging skill gaps in managing these digital tools. The paper further investigates the implications of these transformations for different sectors including banking, SMEs, and the construction industry. Each sector faces unique challenges and benefits from the adoption of these technologies. Future trends suggest a continued evolution influenced by technological innovation and regulatory changes. The paper underscores the necessity for ongoing research and adaptive strategies to fully leverage digital advancements in managing financial risks. By understanding these dynamics, financial institutions can better navigate the complexities of the digital age, ensuring robust risk management and a competitive edge in the global market.

PurposeThis study aims to study the connection between financial risk management frameworks (FRMF) and sustainable development practices (SDPs) within Chinese project construction. In addition, this study examines the role that Computerised Accounting Information Systems (CAIS) play in improving projects’ efficiency and resilience. This research also investigates the effect of operating efficiency on the interaction between FRMF and SDPs.Design/methodology/approachThe research utilises data collected through a structured questionnaire survey administered to construction project managers across China. The 249 valid responses were analysed by using the partial least squares structure equation modelling (PLS-SEM) method to evaluate the hypothesis proposed.FindingsThe research results show that FRMF has a positive impact on SDPs, while operational efficiency (OE) significantly influences this connection. In addition, CAIS enhances the effect of FRMF on improving SDPs, making it easier to connect risk management and sustainable practices. The findings confirm the modulating importance of CAIS in boosting the resilience and profitability of construction projects.Practical implicationsThe research highlights the significance of incorporating CAIS in FRMF, which maximises operational efficiency and sustainably achieves results in construction projects. Construction managers and policymakers are encouraged to concentrate on enhancing CAIS infrastructure and training for long-term sustainability and resilient projects.Originality/valueThis study is among the first to empirically explore the dual role of CAIS as a moderator and OE as a mediator in the link between financial risk management and sustainable development practices in the Chinese construction sector. It contributes a novel theoretical framework that combines digital accounting systems with sustainability strategies under conditions of financial uncertainty.

Nigerian banks encounter persistent difficulties in efficiently managing and disclosing credit and liquidity risks, considerably affecting their financial performance and shareholders’ confidence. This study, therefore, examined the effect of risk-management practices and disclosures on the financial performance of Nigerian commercial banks. The population of the study comprised 13 Nigerian commercial banks, of which 12 were purposively chosen, subject to data availability. The data explored in this study originate from World Development Indicators and the annual reports and accounts of the selected Nigerian commercial banks from 2012 to 2023. The data analysis technique used was panel regression analysis, which was further extended to the generalized method of moments in a bid to account for potential endogeneity. The study made use of EViews 12 software to analyse the data. The results reveal that liquidity risk disclosure and firm size had significant and positive effects on financial performance, while credit risk disclosure, credit risk, firm age, and leverage had significant and negative effects. This study concludes that credit risks significantly undermine commercial banks’ financial performance, as an upsurge in non-performing loans results in reduced financial performance. Conversely, effective liquidity risk disclosure characterized by transparent reporting on liquidity position was found to enhance financial performance. This study, therefore, recommends, among others, that banks should strengthen their credit risk assessment framework and enhance transparent risk reporting to improve performance and financial stability.

In a continuously dynamic economy, both risk management and sustainability are elements that must be constantly monitored. This study analyzes the link between risk management, sustainability, and the financial performance of companies through a systematic literature review of key articles from bibliographic databases. A total of 9092 publications indexed in the Web of Science database (2020–2024) were analyzed using bibliometric analysis with VOSviewer. Findings suggest a positive relationship between effective risk management, sustainable business practices, and financial performance. Firms that integrate risk assessment into sustainability strategies achieve greater resilience and improved outcomes. Research highlights the importance of transparency in risk identification and reporting, particularly in sustainability reports, as a driver of long-term performance and value creation. The study also identifies disparities in implementation across industries and regions, with emerging markets facing structural challenges in adopting comprehensive risk–sustainability frameworks. These findings emphasize the need for sector-specific risk strategies and stronger policy support to maximize financial benefits. This research provides valuable insights for financial managers and researchers. The field remains dynamic, offering new perspectives for future studies and policy development.

The integration of Artificial Intelligence (AI) in financial analytics has significantly enhanced corporate finance by improving forecasting accuracy, risk management, and decision-making efficiency. This study examines AI-driven financial analytics, focusing on its transformative role in corporate finance. The research employs a mixed-methods approach, incorporating predictive modeling, regression analysis, and AI impact assessments to analyze financial performance before and after AI implementation. The findings reveal that AI-driven forecasting models improve prediction accuracy by up to 92%, significantly outperforming traditional statistical methods. AI-based risk management systems enhance risk detection rates by 90%, mitigating financial losses more effectively. Additionally, AI-driven decision-making tools reduce processing time by 85%, enabling firms to make data-driven strategic decisions more rapidly. Statistical analysis confirms a moderate positive correlation (r = 0.396) between AI-driven forecasting and financial performance, while regression models indicate that AI-driven risk management (β = 1.246) has the strongest impact on corporate financial optimization. The study concludes that AI-driven financial analytics enhances corporate resilience, improves risk mitigation, and streamlines financial decision-making. It recommends that firms invest in AI-driven financial strategies, enhance data governance, and adopt regulatory-compliant AI frameworks to maximize financial performance.

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Risk management is a critical component of today’s financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic models are inadequate to address the complex structures of the latest data. This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. A combination of quantitative and qualitative ratings derived from the analysis of financial texts results in high accuracy and stability compared with the HFRP model. Evaluating key findings, the quantity of training & testing loss decreased considerably and they have their final value as 0.0013 and 0.003, respectively. According to the hypothesis, the selected HFRP model demonstrates the values of the revenue, net income, and earnings per share (EPS), and are closely similar to the actual values. The model achieves substantial risk mitigation: credit risk lowered from 0.75 to 0.20, liquidity risk from 0.70 to 0.25, market risk from 0.65 to 0.30, while operational risk is at 0.80 to 0.35. By analyzing the results of the HFRP model, it can be stated that the proposal promotes improved financial stability and presents a reliable model for the contemporary financial markets, which in turn helps in making sound decisions and improve the assessment of risks.

Systemic risks are relevant to most segments of financial and economic activity, which creates the threat of financial instability of enterprises. Financial security, in this case, implies the state of financial soundness; it is expressed through various parameters including the level of solvency, financial stability, business activity, and efficiency of management. These questions are specific to each indicator, and this paper aims to understand their relations and how changes in them can result in risks. It elaborates the risk analysis and assessment procedure that uses a set of common financial analysis tools and determines key financial coefficients. Liquidity ratios, solvency ratios, profitability ratios, and the risk exposure metrics are the key areas that the research is based on. Correlating the financial indicators from a rich dataset from Yahoo’s Stock market data, regression analysis is used to determine these relationships between these indicators and risk management factors. A positive relationship between liquidity ratios and profitability is detected points to the fact that, firms with high liquidity levels are more capable of attaining good financial health and returns. It establishes the need for the development of a credible risk management structure that would lower threats to fiscal and economic activities. In light of this, the research shows that the liquidity and solvency management lowers financial risk and improves return for companies. The regression analysis was conducted to ascertain the degree of predictability of each independent variable to the dependent variable and the findings revealed that approximately 94 percent of each independent variable is predictable of the dependent variable that able to establish as much as 5% of the total variability in profitability, with the PE ratios being an indication of this aspect, was attributed to stock prices, as a result, the firm must be keen on its financials. This paper explores the employability of the proposed methods and models to assess the risks with high accuracy and supports optimum decision making in regard with the needed security of the enterprises. By adopting these methodologies, the businesses can observe and estimate risks which exist in the future which would help them to avoid certain issues or dangers and also pave the way for the improvements of the financial stability of the businesses. On this note, the study establishes the fact that incorporating the notion of total risk management into the framework of analyzing business and making financial decisions is inevitable in maintaining corporate financial stability and realizing perpetual economic solidity. This research enriches the literature on financial risk management, as it outlines viable strategies for the protection of enterprise financial sustainability.

This paper presents a comprehensive investigation into the role of prompt engineering in optimizing the effectiveness of large language models (LLMs) like ChatGPT-4 and Google Gemini for financial market integrity and risk management. As AI tools are increasingly integrated into financial services, including credit risk analysis, market risk evaluation, and financial modeling, prompt engineering has become crucial for improving the relevance, accuracy, and contextual alignment of AI-generated outputs. This study evaluates the impact of various prompt configurations in enhancing financial decision-making. Through a series of experiments, the paper compares the performance of ChatGPT-4 and Google Gemini (versions 1.5 and 2.0) in generating actionable insights for credit and market risk analysis. The results reveal that ChatGPT-4 outperforms Google Gemini by over 30% in generating accurate financial insights. Additionally, ChatGPT-4 Version 4 is found to be 20% more effective than Version 3 in risk analysis tasks, particularly in aligning with regulatory frameworks and financial data. These improvements highlight the significant role of prompt engineering in enhancing the precision of financial models. Furthermore, the study explores the reduction of error rates through optimized prompt strategies. In particular, prompt engineering reduces error rates by approximately 20% when assessing complex financial queries.

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With the increasing adoption of technologies such as mobile banking and blockchain, the banking sector in developing and emerging economies is experiencing both opportunities and challenges. This study examines the impact of FinTech on bank risk-taking and profitability in the small island economy of Fiji, spanning the period from 2000 to 2024. We employ a fixed-effects model and conduct robustness checks using random effects, pooled ordinary least squares (OLS), and the generalized method of moments (GMM) method, focusing on seven banks (five commercial banks and two non-bank financial institutions). Our analysis evaluates the effect of FinTech while controlling for other bank-specific factors that may influence risk-taking and profitability. The results indicate that FinTech development significantly reduces bank risk-taking and enhances profitability, suggesting a positive and substantial impact on financial performance and stability. The findings highlight the need for banks operating in Fiji and similar small economies to continue and expand their investments in FinTech innovations. Furthermore, the study suggests that regulatory bodies and policymakers should strengthen institutional and regulatory frameworks to support and guide FinTech’s evolution within the banking sector.

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This study aims to investigate the relationship between competition and bank risk-taking in Indonesian Islamic banking using linear and non-linear approaches. The dynamic panel regression using a two-step system generalized method of moments (GMM) is used to investigate the impact of competition on Z-score and financing loss provision (FLP) as proxies of bank stability and financing risk, respectively. This study uses all Indonesian Islamic banks (IB) from 2015–2020 using quarterly data. The competition positively affects the Z-score and negatively influences the FLP. Furthermore, higher concentration is positively linked to the Z-score but is negatively related to the FLP. This study also incorporates a quadratic term of the competition to examine the competition-stability nexus. The findings indicate that the squared competition is negative for the Z-score and positive for the FLP. In general, this study confirms the competition-stability view. Some policy implications can be drawn from this study. Indonesia needs more players, and they are large IB in the form of full-fledged IB. This research uses all IB in Indonesia, but these results reflect conditions in one country with its own business and political environments. Of course, the findings cannot be generalized for all IB worldwide using cross-country data. To the best of the authors’ knowledge, this is the first study that examines the non-linear relationship between competition and stability using the adjusted Lerner index in IB.

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This paper examines how physical and transition climate risks affect the risk-taking behavior of local commercial banks in China. We construct novel measures of physical risk from high-resolution meteorological data and transition risk from a new China-specific policy uncertainty index. Panel data regressions show that exposure to both risk types significantly elevates banks’ passive risk, active risk-taking, and overall insolvency risk. The economic effects are substantial: a single additional day of extreme rainfall increases system-wide non-performing loans by over 710 million CNY. Our findings are robust to a comprehensive set of checks, including using alternative sample selections, dynamic panel models, and an instrumental variable analysis. Further analysis of the transmission channels shows that the 2016 Paris Agreement reduced bank risk-taking, greater regional banking competition mitigates climate risk shocks, and climate risk is transmitted to banks by damaging the financial health of firm borrowers. These results highlight the critical role of climate-aware financial regulation and market structure in building a resilient banking system.

This study aims to analyze the factors that determine the level of banking risk-taking in Indonesia, focusing on the role of the Capital Adequacy Ratio (CAR), Return on Equity to Total Assets (RETTA), and Loan to Deposit Ratio (LDR). The study employs a multiple linear regression method to examine the influence of independent variables on Bank Risk Taking, measured using Non-Performing Loans (NPL). The data used in this research come from the banking sector in Indonesia, applying a quantitative approach. The findings indicate that CAR has a positive and significant impact on Bank Risk Taking, with a regression coefficient of 3.021. This suggests that an increase in CAR tends to raise NPL, implying that banks with stronger capital tend to take higher risks. Furthermore, RETTA also has a positive and significant effect on Bank Risk Taking, with a regression coefficient of 0.531, meaning that an increase in RETTA contributes to higher NPL. Conversely, LDR has a negative and significant impact on Bank Risk Taking, with a regression coefficient of -0.020, indicating that an increase in LDR can reduce NPL. The novelty of this study lies in the simultaneous analysis of three key indicators influencing bank risk-taking, providing a comprehensive perspective on the relationship between capital structure, profitability, and liquidity in Indonesia’s banking sector. These findings have implications for the development of social sciences and humanities, particularly in economics and financial management, by highlighting the importance of banking risk management policies. Recommendations include strengthening regulations and supervision over CAR and RETTA to control higher risk exposure, as well as optimizing LDR to maintain financial stability in Indonesia’s banking sector.

Over the past decade, FinTech has emerged as a pivotal catalyst for diversifying the operational landscape of commercial banks and fostering innovation in their revenue streams. However, the influence of FinTech on bank risk profiles and its heterogeneous efficacy across various credit segments warrant closer examination. Utilizing a dataset encompassing 587 commercial banks from 2015 to 2022, this study examines the impact of FinTech on bank risk-taking, with a focus on credit allocation as a key mechanism through which FinTech influences credit risk management. The study reveals three key insights: (1) FinTech augments banks’ proactive risk-taking while simultaneously curbing their exposure to passive risk; (2) FinTech demonstrates notable efficacy in mitigating risks in micro and small credit and retail credit sectors, with less pronounced effects in corporate credit; (3) The risk-preventive impact of FinTech is particularly pronounced in regional banks, albeit with regional differences in its effectiveness. In light of these findings, we propose that commercial banks actively adopt FinTech advancements and expand their application across various credit domains. Banks are encouraged to capitalize on their unique strengths to refine FinTech integration. Furthermore, we advise regulatory bodies to enhance their supervisory capabilities and cultivate regulatory technology to preempt and mitigate potential systemic risks stemming from FinTech advancements.

The present study examines the role of digital transformation in reducing bank risk-taking, focusing on Chinese commercial banks. It addresses the mediating role of management efficiency and credit structure and includes both a heterogeneity analysis and an assessment of marginal effects. The text mining approach was used to construct a digital transformation index of 113 commercial banks, considering digital strategy as well as digital technology. This research aimed to explore the relationship between digital transformation and bank risk-taking employing panel fixed effects, mediating effects, and quantile regression models. The analysis shows that management efficiency and credit structure are key mediating factors between digital transformation and bank risk-taking. Additionally, digital transformation is more effective in reducing the risk of urban commercial banks, state-owned banks, and listed banks. While the impact of digital transformation increases with the increase of the non-performing loan ratio. The region and industry considered in the study are limited to commercial banks in China, which may affect the generalizability of the research conclusions. Future research could consider different industries and regions to verify and enrich existing research results. The findings underscore the necessity to combine digital transformation with effective organizational management and structural adjustments. Improving management efficiency and optimizing credit structure can enhance banks’ risk management capabilities. This study deeply reveals how digital transformation can reduce the risk-taking of commercial banks through management efficiency and credit structure. It provides valuable insights for academic research and practical applications in the field of management.

PurposeThe impact of banking deregulation on firms and economic growth is heavily researched, but not the effects on banks’ risk-taking. This study aims to investigate the impact of China’s 2009 banking deregulation on bank risk-taking, particularly from a balance sheet capacity perspective.Design/methodology/approachUsing a difference-in-differences approach, this study examines how deregulation affects bank risk-taking. A three-stage regression strategy is employed to conduct mechanism analysis.FindingsThe results reveal that deregulated banks exhibit higher levels of risk-taking. Mechanism analysis confirms the bank balance sheet capacity channel: deregulation helps strengthen the net interest margin of deregulated banks, which enhances their balance sheet capacity and subsequently increases their risk appetite. In addition, deregulation improves firms’ access to long-term credit in regions with limited credit availability, especially for smaller firms, thereby expanding the financial sector’s service outreach.Practical implicationsWhile banking deregulation enhances credit availability for firms and supports the real economy, it also raises banks’ risk-taking, posing challenges to financial stability. Our study highlights the trade-off between supporting the real economy and maintaining financial stability under banking deregulation.Originality/valueThis study fills a gap in research on the effects of banking deregulation on bank risk-taking, highlighting the critical role of balance sheet capacity in this process.

This study aims to examine the effect of CEO power, foreign ownership, independence, and voting rights on financial performance in banks with risk-taking as a moderating variable after the financial crisis in 2007-2008. The method used is quantitative using secondary data. The research data source uses the financial statements of Indonesian companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2022. Sample collection used a purposive sampling approach. Data was obtained from the company’s financial reports on IDX and official websites. Data analysis uses panel data regression. The author found that CEO power, independence, and bank risk taking had a significant negative effect on the financial performance of banks, while foreign ownership and voting rights did not have an effect on financial performance of banks. This study contributes to the literature on bank governance and risk-taking. The purpose of this empirical analysis is to examine in detail the subject and the dynamics among these variables in the macroeconomic environment of the financial system, particularly with regard to the regulatory and supervisory framework after the financial crisis in 2007-2008.

This study investigated the nonlinear effects of environmental quality (EQ) on bank risk-taking in the region and the moderating influence on the EQ risk-taking nexus. The study employed generalized quadratic quantile regression techniques to unearth nuanced relationships within 154 publicly traded banks from 10 MENA region countries between 2011 and 2023. The results suggested a U-shaped relationship between bank risk-taking and environmental quality proxied by the environmental performance index EPI. This outcome indicated that initial advances in environmental quality were associated with reduced risk-taking, which may increase beyond a certain threshold. Furthermore, the moderating influence of bank size was substantial, as larger banks typically exhibited a more robust ability to manage environmental hazards without increasing their risk-taking behaviour. The implications for policy and practice are substantial, indicating that developing effective risk management strategies should consider both the size of banks and environmental improvements.

Although scholars drawing on the classic agency framework predict a positive relationship between board independence and bank risk‐taking, scholars adopting a contingency approach predict that such a relationship may not be positive. This theoretical debate is reflected in the inconclusive empirical evidence. In this study, building on the new institutional economics tradition, we go beyond a universal approach to our focal relationship and further advance a contingency approach to the agency framework.In a retrospective meta‐analysis combining 81 primary studies from 2002 to 2022 that cover 106 countries, we find that the board independence–bank risk‐taking relationship is not universal but contingent on the institutional conditions facing the bank. Specifically, we find that our focal relationship is positively moderated by the level of depositor monitoring and negatively moderated by regulatory stringency. Moreover, we find that the effect of depositor monitoring is negatively moderated by regulatory stringency.Our study simultaneously relaxes two central assumptions of classic agency theory and shows that formal institutions, and in particular those protecting stakeholders' interests, can directly affect independent directors' efficacy as shareholder agents. We also point out the ability and incentives of independent directors to monitor in the interest of shareholders as the relevant causal mechanisms. Lastly, we add to research adopting a polycentric view of institutions and complement a burgeoning research stream examining corporate governance bundles.Our study adds to the debate among practitioners and policymakers on what constitutes good bank governance. It also intersects the debate on the relative merits of market and regulatory discipline in banking.

This study investigates the dynamic relationship between foreign ownership and bank risk-taking within the Canadian banking sector. The purpose of the research is to assess whether increased foreign participation in bank equity has a stabilizing or destabilizing effect on institutional risk levels. Given the progressive liberalization of ownership structures in Canada's banking industry, the study provides a timely analysis of how international capital affects risk behavior in a mature financial environment. The central aim is to explore whether foreign stakeholders act as risk-mitigators or risk-enhancers in the operational strategies of commercial banks. To explore this relationship, the research adopts a panel data methodology using information collected from a sample of Canadian commercial banks over a multi-year period. The model specification includes lagged dependent variables and a series of financial and macroeconomic control variables. Three estimation techniques—Ordinary Least Squares (OLS), Random Effects (RE), and the Generalized Method of Moments (GMM)—are employed to ensure robustness and correct for potential endogeneity. The inclusion of lagged risk indicators helps capture temporal stability effects, while foreign ownership ratios are introduced to isolate the influence of international participation in equity holdings. In addition, macroeconomic indicators such as inflation and GDP growth are used to control for external financial shocks that may influence bank behavior. The results indicate a statistically significant and consistent negative relationship between foreign ownership and bank risk. In all model specifications, banks with higher levels of foreign equity participation demonstrated lower risk, as measured by standard financial stability metrics. These findings suggest that foreign owners, by bringing improved governance standards, global oversight practices, and reputational discipline, help reduce risk-taking behavior in the banking system. Furthermore, the presence of lagged positive effects in the model implies that previous periods of stability reinforce future risk moderation, pointing to a cumulative effect of prudent banking practices. This pattern is particularly pronounced in larger banks, where international partnerships are more common and oversight structures more developed. The study concludes that foreign ownership in Canadian banks serves as a constructive force for risk management, offering valuable policy implications for financial regulators and institutional investors seeking to enhance resilience in banking systems.

This study examines the impact of ownership concentration (OC) on bank credit risk in Türkiye, using panel data from 14 commercial banks covering the period 2003–2020. Employing panel regression analysis, the findings show a positive and significant effect of OC on non-performing loans (NPLs), suggesting that higher OC increases credit risk. Control variables include bank size, return on assets, deposits, consumer price index (CPI), and GDP growth. The results highlight OC’s dual role in both enhancing monitoring and increasing risk-taking.

Instability in the banking sector can trigger a domino effect on the broader economy. Therefore, monitoring banks' risk-taking behavior is a crucial part of supervisory and economic policy development. This study aims to analyze how monetary and macroprudential policies influence banking risk-taking in Indonesia from 2015 to 2024. Financial stability is not solely determined by interest rate policy but also by macroprudential tools that help mitigate systemic risks. This research employs the System Generalized Method of Moments (GMM) with a sample of 33 commercial banks listed on the Indonesia Stock Exchange. The findings indicate that monetary policy (CBRATE) and macroprudential policy (MAPP) significantly impact banking stability, measured by the Z-score. When tested together, both policies work in a complementary manner, suggesting that coordinated policies can enhance the resilience of the financial system. Additionally, profitability (ROA) boosts bank stability, whereas exchange rate depreciation (ER) tends to undermine it. These results demonstrate that the synergy between monetary and macroprudential policies is vital for maintaining banking stability in Indonesia, particularly during global and post-pandemic periods.

The study examined bank-specific determinants of bank risk in the Ethiopian banking sector between 2010–2011 and 2021–2022. We used static and dynamic estimation techniques. The data were collected and compiled from the financial statements of each sampled commercial bank and the annual report of the National Bank of Ethiopia. The study revealed that the bank size was inversely related to bank risk. The researchers also found that bank risk was negatively and significantly affected by human capital efficiency and the loan-to-deposit ratio. Additionally, the study noted that when bank loan growth, the bank risk increased in commercial banks. Bank capital ratio and income diversification were not the major determinants of bank stability in the Ethiopian banking sector. The study provides suggestions for bank managers to have efficient risk management strategies, regulators to enhance regulatory frameworks, investors for decision-making, and policymakers to create macroprudential policies to manage risks effectively.

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