系统动力学与审计

审计整改是审计工作的重要组成部分,是衡量审计效果的关键环节,它的效果直接关 系到审计工作的质量。本文运用系统论的整体性、关联性、层次性与动态性原理特 点,深入分析了当前公立医院整改工作的缺陷,并提出了具体优化举措：建立统筹协 同机制破除部门壁垒,以绩效评定和干部任用相结合的方式激发整改动力,规划清晰 的责任清单和问责机制压实各级责任,依托信息化平台和动态跟踪体系实现闭环管理 ,从而推动公立医院审计整改模式由被动应付转向主动治理,从而帮助医院规范审计 工作,不断改进自身管理水平,为公立医院高质量发展提供重要支撑。

为了提升企业合同管理审计的规范性,本文基于建筑企业常见的合同类型及特殊性,先系统分析合同管理审计在风险防控、流程规范、资产保障及竞争力提升等方面的核心作用,然后从提升认知水平、完善制度体系、强化执行力度、推进信息化建设及建立长效监督机制等几个方面提出具体的优化策略,旨在为企业推动合同管理审计与内部控制体系深度融合、提升经营管理水平提供实践参考。

探索"OBE+通专融合"理念引领下的审计学课程改革路径,是破解当前人才培养与行业需求脱节难题,实现教育成果向职业能力高效转化的必然选择。本文基于"OBE+通专融合"理念,针对审计学课程开展教学改革研究与实践。文章首先阐述了OBE理念与通专融合的内涵及其耦合关系,分析了当前课程教学中存在的不足。在此基础上,从顶层设计明确通识教育目标,并提出一系列改革举措:创建"BOPPPS"混合式教学模式,强化教学过程互动与反馈;以"五会"能力为导向重构教学内容,突出思维、学习、沟通、动手与生活能力的综合培养;建立多元化考核体系,推动教学持续改进。

数字技术深度渗透推动财务审计行业向智能化加速转型,但当前研究多聚焦局部技术优化,存在全流程体系化构建不足、技术-业务协同机制缺失、动态风险感知能力薄弱等瓶颈。本文以大数据、AI及RPA技术为核心驱动,构建覆盖数据治理、智能核算、风险预警、决策支持的全流程闭环审计体系,通过多源异构数据融合、自动化流程重构与智能模型嵌入,实现审计逻辑从“规则依赖”向“数据洞察”的跃迁。企业级实践表明,该体系可有效提升审计资源配置效率,增强风险识别前瞻性,为行业智能化转型提供理论支撑与实践范式。

随着数字技术深度融入企业管理体系,国有企业的审计监督机制在效能提升、信息防护、专业化人才储备及系统协同性建设等方面呈现显著制约瓶颈。本文通过分析数字化转型与内部审计的理论关联,揭示了传统审计模式在数据规模、时效性及覆盖范围上的局限性,并探讨了国有企业内审数字化过程中存在的认知不足、技术与管理短板等问题。研究提出,通过提升管理层对数字化内审的重视、构建技术-管理-人员协同的数据安全体系、培养复合型审计人才、整合数字化平台等协同路径,能够有效推动国有企业内部审计的转型升级。本文的结论为国企优化审计效能、强化风险防控提供了理论支持与实践路径,助力其在数字化转型中实现可持续发展。

在数字经济与政策驱动下,企业内部审计数字化转型成为高质量发展关键。本文立足数据驱动视角,结合相关理论,分析转型推进态势与核心突破,指出数据治理滞后、技术适配不足、组织人才支撑薄弱、流程逻辑脱节四大痛点。据此提出“数据筑基、技术赋能、组织保障、流程适配”的系统性破解策略,并构建“战略规划-数据整合-技术深化-生态构建”的分阶段建设路径,为企业突破转型梗阻、释放审计价值提供理论指引与实操参考,助力审计从“监督型”向“价值创造型”转变。

人工智能技术的迅猛发展正深刻重塑国家审计监督的实践范式。本研究聚焦于数据智能分析、自动化流程重构与多源数据融合三大核心机理,系统阐释了AI技术如何通过异常检测模型提升审计发现精准度、借助自然语言处理技术实现审计底稿自动生成、运用知识图谱技术增强审计穿透力。针对不同层级审计机关的差异化需求,研究提出构建分层分类的AI工具开发框架,并设计了包含模型效能评估、算法透明度审查与伦理风险防控的动态优化机制,为破解数据爆炸与审计信息处理能力矛盾、突破传统审计模式适应性瓶颈提供了理论支撑与实践路径。

随着生态文明建设的深入推进,自然资源离任审计与生态补偿制度成为落实绿色发展理念的重要政策工具。本文基于公共产品等理论,探讨两者的联动逻辑与实践路径。研究发现,两项制度在政策目标、实施机制上存在协同空间,但也面临审计标准碎片化、补偿绩效审计缺位等问题。通过案例比较,揭示了审计结果对补偿标准调整、责任落实的推动作用。研究提出构建“审计-补偿-反馈”闭环机制等对策,为提升自然资源治理能力提供参考。

本文针对事业单位内部审计与绩效考核相互脱节的现状，系统研究了两者联动机制的构建问题。文章分析了事业单位内部审计与绩效考核的现状及存在问题，发现内部审计职能作用未充分发挥、绩效考核体系尚不完善、审计结果未有效融入绩效考核、绩效考核数据未充分支持内部审计等问题。在此基础上，构建了内部审计与绩效考核联动机制的总体框架，设计了内部审计支撑绩效考核、绩效考核反哺内部审计以及两者数据共享与协同决策的具体机制。同时，提出了组织保障措施、制度保障措施、能力提升措施和监督评估机制等实施策略，为事业单位构建科学高效的内部治理体系提供了理论支撑和实践指导。研究表明，内部审计与绩效考核联动机制的构建有助于实现单位内部治理资源的优化配置，促进监督评价与激励约束功能的有机融合，提升单位整体管理水平和运行效率。

本文聚焦数字化转型背景下企业财务风险治理模式的变革需求,探讨审计监督与财务 风险管控的协同机制及其作用路径。研究以内部控制理论、风险管理理论与信息不 对称理论为支撑,分析审计监督从合规检查向风险预警延伸、财务风险管控从静态控 制向动态监测转变的逻辑必然性,提出以数据共享为基础、风险识别为核心、持续监 测为保障的协同治理体系。通过构建覆盖“风险识别—监督评价—反馈改进”的闭环模 型,论证数字化工具在提升审计效率与风险管控能力中的关键作用,为企业优化财务 风险治理提供理论支持与实践路径。

《工程学研究与实用》(ISSN: 2717-5316)是一本开放获取、在线出版的国际中文期刊。本刊刊发范围包含全工程学科及工程学教育的研究与实用，广泛涉及地矿类、材料类、机械类、仪器仪表类、能源动力类、电气信息类、土木建筑类、水利类、测绘类、环境与安全类、化工与制药类、交通运输类、海河与船舶、航空航天类、武器类、工程力学、生物工程类、轻工纺织食品类、农业工程类、林业工程类及公安技术类的各项相关工程开发与应用。

本文针对高校异地研究机构的审计监管问题，以某大学为例，分析其在跨地域运营中面临的管理挑战与潜在风险。研究指出，异地机构虽在产学研合作与区域创新中发挥重要作用，但也存在监督不足、内控制度待完善、考核机制单一等管理问题。通过年报审计、专项审计等多种类型审计，揭示了在采购、合同、资产、科研项目等多方面的风险。基于此，研究建议强化制度建设、分类管理、内控监督及审计指导，并优化考核机制，以保障合规运营、资源高效利用及风险防范，推动异地研究机构高质量发展。

在企业的流动资产中,应收账款占据了相对较大的比重。如果应收账款不能按时回收 ,企业的发展将会受到阻碍。本文先概述了内部审计在应收账款管理中的作用,通过 具体案例分析,对某矿业公司的应收账款管理现状进行了深入探讨,认为该矿业公司 陷入了应收账款管理的困境,最后探索了走出应收账款管理困境和防范风险的策略, 期望能为相关行业提供参考和借鉴。

数字经济时代,数智化转型已成为企业内部审计提升治理效能的必然选择。本文从外 部动因与内部动因两个维度剖析了转型驱动因素：外部动因基于PEST框架,包括政治 、经济、社会与技术动因；内部动因则从公司治理、内部控制、内审业务发展、价 值创造四个层面展开分析。在此基础上,揭示了转型中面临的战略模糊、数据孤岛、 模型脱节、人才短缺与协同不足等困境,进而构建了“战略引领—数据奠基—技术应用 —人才与绩效升级—协同保障与安全维护”具有针对性的实现路径。

随着数字技术与实体经济的深度融合,国有企业经营管理的数据化、复杂化对传统内 部审计模式提出了严峻挑战。2026年审计署重点科研课题明确提出“国有企业智能化 穿透式内部审计监督的路径与策略研究”方向,标志着审计监督向智能化、精准化转 型已成为行业共识。本文立足于数智时代背景,首先界定了智能化穿透式内部审计的 内涵与理论架构,深入剖析了当前国有企业内部审计面临的“信息孤岛”与“监督时滞 ”等痛点；其次,重点探讨了大数据、人工智能、区块链等智能化技术在穿透式审计 中的应用路径；最后,从体系构建、流程优化及保障机制三个维度,提出构建“数据驱 动、实时在线、精准洞察”的新型内部审计监督体系的对策建议。研究旨在为国有企 业提升风险防控能力、实现审计监督全覆盖提供理论参考与实践指引。

本文系统探讨高等院校固定资产管理领域的现存矛盾与核心问题,重点阐释强化内部审计监督机制的实践价值,并提出优化审计框架的可行路径。通过建立长效审计机制与动态整改反馈体系,推动高校形成涵盖制度优化、权责明晰及核算规范的全流程闭环管理模式,最终实现资产配置效率提升与资源集约化利用,保障资产安全性与效益可持续增长。

 在“双碳”战略背景下，新能源企业日益成为推动绿色低碳转型的关键力量。ESG（环境、社会与治理）治理结构作为衡量企业可持续发展水平的重要制度安排，对新能源企业的战略落地与风险管理提出更高要求。内部审计因其独立性与专业性，在支撑企业ESG治理质量提升方面具有独特优势。本文围绕内部审计嵌入新能源企业ESG治理结构的路径展开研究，系统分析当前嵌入过程中的职能割裂、能力错配、规范缺失等现实困境，提出构建ESG导向的审计框架、完善组织协同机制、强化审计专业能力、推动数字化赋能与健全审计成果应用体系等路径建议。研究认为，推动内部审计与ESG治理的深度融合，有助于提升新能源企业ESG信息的可靠性与治理执行力，构建更具透明性与韧性的可持续发展体系。

在企业组织架构日趋复杂、治理要求持续提升的背景下,传统内部审计存在监督层级 模糊、资源配置失衡、响应效率偏低、整改闭环不足等问题,难以适配多层级、跨职 能、广分布的现代企业运营格局。本文立足组织治理与风险管控逻辑,剖析内部审计 与组织结构、风险传导的内在关联,构建职能分层、权责分级、流程分类、智能监测 四位一体的内部审计分层管控体系,提出分层授权、穿透监督、动态预警、闭环整改 的实施路径,为企业提升内审监督质效、强化风险防控、完善公司治理提供可操作方 案。

内部审计是国有企业健康、可持续发展的重要保障。加强国有企业内部审计监督，对于推动贯彻落实宏观政策措施和决策部署，推进国有企业反腐倡廉和党风廉政建设，推动深化国有企业改革，防止国有资产流失，推动深化国有企业国有资本审计监督，完善国有企业内部管理与控制，提升国有企业管理决策的科学性，促进国有经济持续、健康发展均具有非常重要的意义。本文通过简要分析国有企业内部审计监督的现状，对现存的问题与不足进行剖解分析，以探究提升国有企业内部审计监督问题的解决措施和优化思路。

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Adoption of artificial intelligence (AI) in the auditing sector is influenced by ambiguous interaction of various factors that impact the input, processing, and output stages of AI adoption representing a complex system. System dynamics, one of the areas of system thinking, provides a comprehensive framework for understanding the dynamic behaviour and interactions of such complex systems. In the present research, the adoption of AI in auditing has been analysed through system dynamics. A conceptual framework of AI adoption has been proposed to comprehensively understand the process of AI adoption in auditing. Systems dynamics tools such as causal loop diagram (CLD) and stock and flow diagram have been deployed to study the complex and unclear interactions of factors influencing adoption. To simulate and model AI adoption in auditing, Vensim and Stella software were utilised to construct a CLD and a stock and flow model, offering novel understandings into the drivers and inhibitors of AI integration. This study provides a foundational understanding for practitioners and researchers navigating the transformative impact of AI on auditing.

Recently, artificial intelligence (AI) technologies and digital transformation concepts have led to changes in internal audit activities. While traditional audit techniques are usually performed without much use of technology, new digital audits complete these processes by using technological infrastructures. This transformation changes the way auditors work and thus creates a new value system in audit processes. This new value system is the artificial intelligence assisted audit system. Artificial intelligence assisted audit systems help to detect potential risks that may occur in enterprises at an earlier stage. Thus, it will increase the reliability of financial reporting of enterprises. It will significantly reduce the time spent to detect errors and irregularities in businesses. It will also enable auditors to focus on value-added activities instead of routine and time-consuming operations. In particular, situations such as incorrect analysis in audit activities, ethical problems that may occur, etc. appear as negative situations arising from technological developments and artificial intelligence. In addition, the existence of dangerous situations such as the loss of the importance of security and confidentiality in audit activities creates the necessity to establish and establish new control mechanisms in terms of technological systems. This study aims to determine the effects of artificial intelligence and technological systems on internal audit activities, especially auditing. For this purpose, field research was conducted in the literature. The study concludes that digital transformation and artificial intelligence contribute to making internal audit functions more efficient and reliable, but also bring new risks.

The article presents a comprehensive study of the functioning and development of internal control systems (ICS) in enterprises under conditions of digital transformation of the economy. It is determined that the modern ICS goes beyond the traditional functions of verifying the reliability of financial information and compliance with regulatory requirements, transforming into a strategic element of corporate governance. It integrates the processes of accounting, auditing, risk management, information security, and data analytics, forming a unified information and control infrastructure of the enterprise. The focus is placed on the role of digital technologies – ERP, CRM, BI platforms, and Robotic Process Automation (RPA) – in increasing the efficiency and transparency of internal control. It is shown that ERP systems (SAP, Odoo, Microsoft Dynamics 365) ensure end-to-end business process integration, allowing for automated control of procurement, accounting, sales, and inventory management. Integration with CRM platforms (Salesforce, Zoho, HubSpot) supports the development of customer interaction control systems, improves service quality, and identifies risky operations. The use of BI systems (Power BI, Tableau, Qlik) enables the creation of interactive monitoring dashboards that display key performance indicators (KPI) in real time. Special attention is given to the potential of RPA technologies, which provide automatic verification of primary documents, data reconciliation between ERP and accounting subsystems, report generation, and monitoring of risky transactions. It is substantiated that the implementation of RPA allows reducing human intervention, increasing control accuracy, minimizing time costs, and enhancing the analytical flexibility of management decisions. It is emphasized that the digital ICS is becoming a dynamic system capable not only of detecting deviations but also of predicting potential risks through artificial intelligence (AI) and machine learning (ML) technologies. Such systems transform internal control from a reactive tool into a proactive mechanism of corporate security that ensures stability, flexibility, and competitiveness of enterprises in the digital economy. The obtained results can be used for developing methodological recommendations for the implementation of intelligent internal control systems, improving corporate risk management standards, and training specialists in digital audit and control.

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Building on semi-structured interviews and publicly available documents in the realm of accounting, auditing and capital market regulation in Romania, this paper reviews and reflects on the prerequisites for, and conditions affecting the development of a financial reporting enforcement system (FRES) of Western origin in an emerging economy. It does so by examining institutional factors within and across the key components of the Romanian FRES, namely the engagement of the preparers and auditors of corporate financial reports and their interactions with public oversight bodies. The creation and functioning of the Romanian FRES are driven by the dynamics between Western and local pushes and pulls. Western actors offered support, especially in terms of technical assistance and educational programmes, but the Romanian government delayed the implementation of local support mechanisms, such that practices and mindsets did not change initially. Although practices and institutions have evolved since the country joined the European Union in 2007, the pursuit of a functional Western-based FRES remains an on-going process that is highly dependent on both the continuous external provision of adequate resources and the enrolment of national actors in the deployment of these resources.

PurposeThis study investigates the performative role of calculative practices in urban decision-making by combining simulation tools and accounting measures. Specifically, this study proposes both theoretical and practical approaches to support the development of an integrated approach for formulating urban sustainability and circular economy policies.Design/methodology/approachThis study combines performativity theory with Systems Thinking and System Dynamics, presenting findings from two simulation sessions focused on developing sustainability and circular economy policies for a virtual urban environment. A System Dynamics simulator (interactive learning environment) was used to facilitate the simulations and support decision-making.FindingsThis study demonstrates the potential of combining accounting and simulation principles (specifically, Systems Thinking and System Dynamics) to enhance interactions between human agents and support decision-making through a rigorous and quantified simulation model. It also proposes an approach that fosters the integrative potential of calculative practices in urban sustainability decisions.Originality/valueThis study offers a novel approach by combining accounting concepts with Systems Thinking and System Dynamics principles and tools to facilitate human-agent interaction and support decision-making in complex and dynamic environments, such as urban sustainability. It specifically examines circular economy policies in cities and provides new insights into applying performativity theory in this context, thereby offering novel practical implications.

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Based on integrating the theories of technology, society's education and systems Dynamics to challenge the "personalityisation/precision" rhetoric in AI education. How black-box Decisions, dependent on Data and Context De-Embedding Can Turn Technical Bottlenecks Into Ethical and Social Harm Paths That Include Weakened Educational Agency, Algorithm Discrimination, And Increased Surveillance. Then present the Three-dimensional framework of responsible educational artificial intelligence from three aspects: Technical dimensions, focus on interpretability, Auditability, human-in-the-loop control, including audit logs and failure mode mitigations, as well as an ethical dimension combining life cycle environmental impacts assessment and constant supervisions for governance dimensions comprising regulations, industries self-inspection, schools-communities cooperate activities, etc., through interdisciplinary research. The computational verification of Vcpl on ASSISTments, Ednet and oulad indicates an AUC value of 0.82 and NLL of 0.44 with a fairiness gap reduced to 0.05 and safety violations decreased to 1.1%; Also examined the privacy budget ε_p - utility trade-off for informing deployments and rollbacks.

The article examines the application of artificial intelligence technologies for regulatory risk monitoring in the context of internal audit function transformation. Based on the analysis of regulatory activity dynamics in the Russian Federation for 2020-2025 (from 505 to 694 federal laws annually) and statistical data on regulatory environment complexity (over 500 thousand mandatory requirements for large companies), the insufficiency of traditional compliance methods is substantiated. Key AI technologies relevant for regulatory monitoring tasks are systematized: natural language processing, machine learning, optical character recognition, graph databases, robotic process automation. Examples of these technologies implementation in Russian financial sector companies are presented. An AI-based monitoring system architecture integrated with risk management processes and continuous audit is developed. An original system of criteria for AI systems effectiveness assessment is proposed, including five blocks of indicators (data quality, model accuracy, reliability, security, business efficiency) and an audit testing methodology. The results can be used by internal audit services, compliance departments and RegTech solution developers to improve regulatory risk management efficiency.

This study investigates the association between economic uncertainty and audit quality in the BRICS nations, examining both input-based (e.g., audit fees, auditor tenure) and output-based (e.g., restatements, total accruals) measures of audit quality. Utilizing a dataset of 83,511 firm-year observations from 1995–2022, it reveals a significant negative impact of economic uncertainty on audit quality. Additionally, the research explores the moderating role of CEO power, employing principal component analysis to merge various indicators of CEO influence. Findings indicate that powerful CEOs can mitigate the adverse effects of economic uncertainty on audit quality, suggesting a U-shaped relationship between CEO power and audit quality. Methodologically robust, employing techniques like two-stage least squares (2SLS) and two-stage system generalized method of moments (system GMM) to address endogeneity, the study offers a comprehensive analysis of audit quality in the context of economic fluctuations and corporate governance, contributing significantly to the understanding of these dynamics in emerging economies, particularly in the diverse and influential BRICS nations. This study’s findings have significant implications for stakeholders and policymakers, providing insights that can inform policy decisions and enhance corporate governance frameworks.

Due to the complex structure of certification schemes the risk of flaws and scandals is generally high. It has further increased by several developments during the last years. With regard to their potential effects, it is questionable whether the certification approaches are actually able to detect deficiencies within the system and thus prevent crises which may lead to its breakdown. Hence, the ability of a standard to meet its objectives of food quality and safety needs to be enforced. In this contribution we launch the implementation of a controlling tool which automatically monitors audit quality based on information of the respective data bases. By analysing possible negative influences, opportunistic behaviour can thus be detected.

This research performs a critical deconstruction of the internal dynamics and oversight effectiveness of the Audit Committee (AC) within State-Owned Enterprises (SOEs) following the implementation of Minister of SOEs Regulation No. 02 of 2023. Using a Systematic Literature Review (SLR), this study synthesizes agency theory, financial reporting frameworks, and strategic auditing models to evaluate oversight in Indonesia’s two-tier system. The analysis reveals that AC effectiveness is constrained by pathological information asymmetry and the risk of managerial override. Key findings indicate that board independence is more effective in mitigating Real Earnings Management (REM), whereas legal enforcement primarily addresses income-increasing Accrual-based Earnings Management (AEM). The study concludes that strengthening internal controls without direct oversight creates a "false sense of security," potentially increasing fraud volume. Policy implications focus on standardizing functional communication between the AC and the Internal Audit Unit (SPI) to mitigate the double agency dilemma. This contribution emphasizes the need for a shift from compliance-based to substantive strategic oversight.

Abstract This paper analyses the role of internal control systems and internal audit in the architecture of contemporary corporate governance, in relation to the dynamics and volatility of the current global economy. There is a transposition of the internal audit function from a simple compliance check to a strategic value generation tool. The research highlights that responsibilities for maintaining organizational integrity, ensuring transparency, and managing risk have become central pillars in modern strategic management. Also, the study investigates the relationship of complementarity between internal control and internal audit, following how they work together to substantiate and evaluate governance and risk management procedures. The results demonstrate that a strong internal control system not only facilitates the achievement of the entity’s objectives, but actively contributes to strengthening a culture of accountability, being essential for achieving optimal performance. The methodology adopted is a mixed one, combining the bibliometric analysis of the literature - to map the evolution and interconnections of the key concepts - with empirical research based on a structured questionnaire and data processing through statistical techniques in SPSS. The conclusions confirm the deep interdependence between internal audit and internal control, highlighting their combined impact on the effectiveness of corporate governance in the current context.

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The article addresses the problem of dynamic assessment and forecasting of information security risks driven by the growing role of the human factor amid business-process digitalization, hybrid work models, and a changing access context. The purpose of the study is to improve the accuracy and timeliness of risk management for personnel, enhance access controllability through adaptive policies, and advance audit transparency by integrating an RBAC-blockchain into a closed self-learning loop. The object of the study is the process of dynamic information-security risk analysis in corporate systems with personalized consideration of user behavior. The subject of the study comprises the methods and procedures for constructing a multidimensional feature vector, building a user digital twin, designing risk-adaptive access policies, and implementing system self-learning mechanisms. The authors emphasize that static access-control approaches and periodic audits do not match the dynamics of threats and the contextual nature of resource use. The article analyzes the contemporary components of the process model: (f₁) construction of a multidimensional resource-classification matrix; (f₂–f₃) collection, unification, and normalization of behavioral and technical data into the feature vector Q; (f₄) forecasting risky events using a user digital twin with access transactions recorded on an RBAC-blockchain; (f₅–f₆) generation of adaptive countermeasures and delivery of personalized policies and training content; and (f₇–f₈) Fback feedback collection and self-learning with adjustment of weights, models, and access rules. It is shown that combining statistical methods, machine-learning algorithms, and immutable blockchain logging ensures reproducible auditing, reduces the “risk window,” and supports continuous trust validation in line with Zero Trust principles. A scheme for triggering countermeasures is proposed based on the probability matrix R and the resource’s criticality class. Procedures for fine-tuning and transfer learning are described to keep models current without excessive computational costs. Particular attention is paid to personalized dashboards and multichannel delivery of recommendations that shorten user response time. The importance of qualitative Fback metrics (e.g., user satisfaction and content clarity) is emphasized for revealing elements of security culture. Thus, applying the developed process model establishes an “analysis–forecast–action–feedback–self-correction” cycle that improves the accuracy of risk assessment, enhances response timeliness, and advances transparency in access governance. The results can be integrated into SIEM/UEBA environments, access-management systems, and corporate programs for improving personnel cyber literacy.

This study explores the relationship between financial reporting quality and insurer governance, with the hypothesis that robust governance procedures exert better control over managers’ opportunistic behavior. The analysis is based on a dataset of insurer firms from 2014 to 2021. The econometric results obtained using the two‐step system GMM technique reveal that the overarching influence of corporate governance on enhancing financial reporting quality is evident, with board and risk governance matters the most. Among individual governance attributes, the optimal board size, a higher proportion of independent directors, audit and risk committees’ size, and risk committee independence play a significant role in governing discretionary accruals. The efficacy of governance mechanisms considerably differs across life and non‐life insurers, shedding light on the nuanced dynamics within the Indian insurance market. The results lend empirical support to resource dependency and agency theories within the Indian insurance sector. The implications suggest potential avenues for amending or redesigning governance norms with specificities of insurers and the ultimate goal of fostering an environment conducive to enhancing the reporting quality of Indian insurance firms.

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The article presents a system dynamics simulation model for forecasting the effectiveness of strategic human resource management practices in the public sector. The model is developed based on empirical data collected in 2025 in the Volgograd region (a survey of 1,250 civil servants, 45 in depth interviews, 8 focus groups, and reports from control and audit bodies). Key model variables include the integral index of HR practices, employee engagement (UWES-9), the share of appointments from the personnel reserve, and the achievement of national project KPIs. Parameter calibration is performed, and three scenarios (inertial, baseline, optimistic) are analyzed up to 2028. It is found that the inertial scenario does not move the system out of the critical zone (average integral index in 2028 – 2,32). The baseline scenario (step-by-step implementation of a set of measures) ensures an increase in the average integral index from 2,04 to 3,50 (+ 72 %), the share of appointments from the reserve from 18% to 60%, engagement from 2,9 to 4,1 points, and KPI achievement from 65 % to 82 %. Sensitivity analysis reveals the greatest influence of the speed of HR analytics implementation and top management support. The probability of achieving an average integral index > 3.0 is estimated at 0,97 (Monte Carlo method). The model can be used by regional competence centers to justify HR decisions.

Abstract Multiple strategies have been used in the National Health System (NHS) in England to reduce inappropriate antibiotic prescribing and consumption in order to tackle antimicrobial resistance. These strategies have included, among others, restricting dispensing, introduction of prescribing guidelines, use of clinical audit, and performance reviews as well as strategies aimed at changing the prescribing behaviour of clinicians. However, behavioural interventions have had limited effect in optimising doctors’ antibiotic prescribing practices. This study examines the determinants of decision-making for antibiotic prescribing in hospitals in the NHS. A system dynamics model was constructed to capture structural and behavioural influences to simulate doctors’ prescribing practices. Data from the literature, patient records, healthcare professional interviews and survey responses were used to parameterise the model. The scenario simulation shows maximum improvements in guideline compliance are achieved when compliance among senior staff is increased, combined with fast laboratory turnaround of blood cultures, and microbiologist review. Improving guideline compliance of junior staff alone has limited impact. This first use of system dynamics modelling to study antibiotic prescribing decision-making demonstrates the applicability of the methodology for design and evaluation of future policies and interventions.

The object of research is the process of assessing the impact of digital transformations on the rationality of financial decisions (RFD) in the context of the functioning of modern financial markets. The problem being solved is to specify a scientifically sound approach to the integrated analysis of quantitative, qualitative and simulation characteristics, which allow for accurate determination of the effectiveness of digital technologies in the processes of financial decision-making, taking into account uncertainty, data limitations, behavioral factors and the level of digital literacy. It is proven that the growth of spending on digital technologies is closely correlated with the growth of the RDF level, which is confirmed by the results of multi-criteria analysis, ARIMA forecasting, Monte Carlo modeling and clustering. The effectiveness of the use of hybrid simulation models that combine system dynamics with an agent approach is determined, and indicators of digital sustainability of companies are also proposed. The recommendations developed to improve the approach to assessing the impact of digitalization of financial markets on RDF can be used by financial institutions, regulatory authorities and the corporate sector to assess the effectiveness of digitalization, increase the validity of financial strategies, reduce the level of risk, adapt to martial law conditions and strengthen competitiveness in the digital environment.

A simplified Building-HVAC system model is presented here. It includes simplified models of building zone and of HVAC equipment. the simplified building zone model is based on a R and C network, whose parameters are adjusted through a frequency characteristic analysis. The implementation of the classical phenomena taking place in building dynamics is discussed. The simplified building model is compared with more detailed models, using the BESTEST procedure. Teh application of the presented model to the audit of commercial buildings is also discussed.

Clouds provide a powerful computing platform is in focus, the security of the current loss of confidence in cloud computing. This study based on the traditional ISO 27001 artificial audit practice, into the waterproof wall in the cloud “information security management model”, with cloud computing development faces serious security problems. In this paper, this study based on system dynamics for information security policy to simulate the cloud computing security scenario and illustrate the cloud computing security prevention procedure. Finally, the simulation results show that the Audit system can provide preliminary prevention of data centers and endpoint host side's breach in the framework of cloud computing. The main contribution has the following three points: 1. The traditional online information security standards, through intelligent automated audit, will gradually update the manual audit mode. 2. Through the introduction of the WcsM waterproof wall concept in this study, the traditional security protection mechanisms (Firewalls, IPSs, IDSs, etc.) will have more proactive measures to ensure cloud information security. 3. The concept of reducing the capital investment by means of automated audits will further promote the importance of SMEs-based small and micro-enterprises.

This paper presents a novel approach for seamlessly integrating system dynamics models into the ChatGPT‐4 conversational AI. By transforming system dynamics models with PySD and embedding them into ChatGPT's conversational context, users can explore even complex models through natural language interactions. ChatGPT‐4 is capable of running simulations based on user‐provided parameters and offering explanations and analyses of the simulation results, leveraging its extensive knowledge base. Because of the constraints of ChatGPT's analytical and computational capacity, it is crucial to provide additional essential information alongside the system dynamics model and to thoroughly verify and implement ChatGPT's responses. ChatGPT, in turn, can dynamically generate executable Python code, creating a bidirectional interface that empowers modelers to engage with ChatGPT using programming languages. This complements existing natural language and image‐based interactions, enhancing the overall efficiency of communication. Successfully integrating system dynamics models with ChatGPT not only makes the models more accessible but also enhances various aspects of system dynamics, including modeling, simulation, discussion, and education. Furthermore, the inherent rigor and transparency of system dynamics models can be extended to ChatGPT conversations, thereby expanding ChatGPT's capabilities.

This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis process at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.

This paper aims to develop an urban transportation planning through the use of system dynamics simulation model. This planning includes congestion mitigation through demand and capacity management, the improvement of public transport sector, and the urban mobility improvement. System dynamics simulation model was used to test and evaluate some alternative policies for urban transportation planning. Simulation results show that mobility depends on travel time, the fulfilment ratio of public transport supply and demand, the effectiveness of public transport transfer distance and transfer time, as well as the access time. Traffic congestion is influenced by the internal factor and external events. This study was conducted at Surabaya, East Java, Indonesia, which is the fourth most congested city in the world. The original scientific contributions of this research include model formulation and development of the daily traffic, urban mobility, and congestion; predictive analysis of the future daily traffic; scenarios development to increase mobility and reduce congestion by considering the internal and external factors. (Received in August 2019, accepted in December 2019. This paper was with the authors 1 month for 3 revisions.)

System dynamics (SD), discrete event simulation (DES) and agent-based model (ABM) are three different simulation modelling methods widely applied to support decision-making in complex systems across various disciplines. However, single simulation modelling approaches can face significant challenges representing the multi-dimensional nature of complex systems composed of interactive and interconnected constituents with dynamic behaviours. Combining different simulation methods offer an opportunity to overcome these challenges and to capture important characteristics and behaviours of such systems. Despite the growing interest and popularity in this approach, guidance for designing and utilizing hybrid models, especially for those combining SD and ABM, is scanty. This paper aims to review and consolidate the existing theoretical guidance/frameworks on combining these two simulation methods. Based on this literature review, we propose an initial framework for combining SD and ABM.

ABSTRACT Discrete Event Simulation (DES) is the main tool used in analyzing supply chains (SCs). In many simulation studies the effect of the modeling approach on SC performance is ignored. Many researches have overlooked the fact that in DES where events are the drivers of variable changes; the modeler has the option to change these variables pre or post delay event occurrence. In SCs this choice is critical and has a significant effect on the results obtained. Another modeling approach is System Dynamics (SD) which overcomes the difficulties of DES because variables do not change in discrete jumps but rather smoothly overtime. Both DES and SD approaches are combined in one model and are compared to two types of DES modeling approaches; Pre-delay and Post-delay DES. A case study is implemented in a local cement SC. Inventory levels (IL), holding costs (HC) and ordering costs (OC) for the Combined DES/SD model are shown to be between those of the other two models. Post-delay DES is shown to overestimate IL and HC but underestimate OC; while, Pre- delay DES is shown to underestimate IL and HC while overestimating OC.

ABSTRACT Computer simulation modeling approach has been used for the last decades to address the growing need for estimation, analysis and improvement of project performance indicators. In this research, a dynamic simulation model is developed capturing both hierarchical and dynamic complexities of projects. This paper discusses how changes in dynamics can influence the critical performance indicators of a project throughout its lifecycle. The designed simulation model is capable of assessing the robustness and suitability of the scope and change management policies. The validity of the model was tested by a real-world project in the petrochemical industry. The significant factors influencing a project’s performance are investigated in the case study. The simulation results indicate that resource allocation decisions could be optimized to improve project performance indicators. The developed decision model can be regarded as a flexible and predictive tool in the construction industry with the purpose of explaining the dynamic behavior of a project. Abbreviation SD: System Dynamics

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.

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The banking sector, as the dominant channel of financial intermediation, has systemic importance for the efficient and stable financial and economic system as a whole. Contemporary academic and professional literature recognises external auditing as an important mechanism for enhancing the credibility of financial reporting and detecting financial fraud in the banking sector. Building on this theoretical framework, this paper applies a bibliometric analysis of scientific publications with the aim of the systematisation and critical examination of the role and scope of external auditing in combating fraud in banks. The analysis relies on a corpus of 130 scholarly articles indexed in the Scopus database, published in the period between 1992 and 2025, and encompasses an examination of publication dynamics, thematic clusters, collaboration networks and dominant research trends. The results indicate a continuous and pronounced growth in scholarly interest in this field, with a predominance of topics related to fraud detection, audit quality and corporate governance. At the same time, research addressing the preventive role of auditing, the implementation of digital and analytical technologies, and studies focusing on less developed economies remains relatively underrepresented, highlighting gaps in the available literature and suggesting potential avenues for future research. The paper contributes to a better understanding of the evolution of the scientific discourse on external auditing and provides a foundation for further theoretical and empirical advancement of research in this area.

The continuous nature of production activities inherent in large complex enterprises imposes its limitations on the management system of such an enterprise. In the article, the production of flat glass is chosen as an example of continuous production. The modern method of producing flat glass is associated with significant energy consumption for starting the process, while the result of control actions is delayed due to the inertia of the processes. The authors chose a fuzzy neural network as a model of the control system, which combines both the advantages of artificial neural networks and the mechanisms of fuzzy logic. A hybrid algorithm is used to train the network. The design of a hybrid network is a constant process of modification and expansion of the network structure, which makes it possible to adapt the model to constantly changing production parameters, making the proposed model the most adaptable to maintain the stability of complex industries.

For more than two decades, the concept of continuous auditing (CA) has been introduced and many large firms had taken the initiatives to apply the CA system in supporting their audit functions. Despite the benefits that the CA system is offers, it is still not widely use and the number of people using the CA system is still considered low. This research focuses on the published papers on the use of CA system within the context of auditing system addressing the quality of system implementation. This paper analyzed primary studies collected using the pre-determined search strings on nine online databases. As a result, a total of 60 articles were carefully selected to undergo further analysis based on empirical evidence of CA system use. The articles were analyzed qualitatively using ATLAS.ti 7 and the elements for the CA system use are extracted from the selected papers. A total of four elements were identified contributing to the use of CA in practice. Those elements are the participant quality, system quality, information quality and products and services quality. This study answers five research objectives to understand the current studies on CA and to determine future research works on CA.

Estimating parameters and their credible intervals for complex system dynamics models is challenging but critical to continuous model improvement and reliable communication with an increasing fraction of audiences. The purpose of this study is to integrate Amortized Bayesian Inference (ABI) methods with system dynamics. Utilizing Neural Posterior Estimation (NPE), we train neural networks using synthetic data (pairs of ground truth parameters and outcome time series) to estimate parameters of system dynamics models. We apply this method to two example models: a simple Random Walk model and a moderately complex SEIRb model. We show that the trained neural networks can output the posterior for parameters instantly given new unseen time series data. Our analysis highlights the potential of ABI to facilitate a principled, scalable, and likelihood‐free inference workflow that enhance the integration of models of complex systems with data. Accompanying code streamlines application to diverse system dynamics models.

Machine learning systems have gained widespread adoption across various industries. This includes highly regulated ones that need to match certain quality requirements based on a given risk exposure. The MLOps paradigm, following a similar approach to DevOps, promises major improvements in quality and speed, with a focus on deploying ML models at a fast pace with high quality on an automated basis. However, traditional point-in-time certifications with manual audits are inadequate for MLOps setups due to frequent changes to the ML system. To overcome this challenge, we propose Continuous Audit-Based Certification (CABC), which uses automated audits to issue or revoke certificates based on an automated assessment of artifacts from the MLOps lifecycle. Our approach utilizes artifacts from the MLOps lifecycle for quality measurements based on standards such as ISO 25012. We propose a risk-based measurement selection, an audit API for standardized retrieval of data for measurement, a tamper-proof data collection process, and an architecture for separation of duties in the certification process. CABC aims to improve efficiency, enhance trust in the ML system, and support highly regulated industries in achieving their quality goals.

Artificial intelligence (AI), which refers to both a research field and a set of technologies, is rapidly growing and has already spread to application areas ranging from policing to healthcare and transport. The increasing AI capabilities bring novel risks and potential harms to individuals and societies, which auditing of AI seeks to address. However, traditional periodic or cyclical auditing is challenged by the learning and adaptive nature of AI systems. Meanwhile, continuous auditing (CA) has been discussed since the 1980s but has not been explicitly connected to auditing of AI. In this paper, we connect the research on auditing of AI and CA to introduce CA of AI (CAAI). We define CAAI as a (nearly) real-time electronic support system for auditors that continuously and automatically audits an AI system to assess its consistency with relevant norms and standards. We adopt a bottom-up approach and investigate the CAAI tools and methods found in the academic and grey literature. The suitability of tools and methods for CA is assessed based on criteria derived from CA definitions. Our study findings indicate that few existing frameworks are directly suitable for CAAI and that many have limited scope within a particular sector or problem area. Hence, further work on CAAI frameworks is needed, and researchers can draw lessons from existing CA frameworks; however, this requires consideration of the scope of CAAI, the human–machine division of labour, and the emerging institutional landscape in AI governance. Our work also lays the foundation for continued research and practical applications within the field of CAAI.

Purpose This paper aims to analyse the implementation challenges faced by internal audit departments of public sector organisations and central banks when implementing continuous auditing (CA) systems. CA aims to monitor internal control systems and risk levels on a continuous basis to support the audit process. This study identifies the implementation challenges of CA systems and proposes adequate countermeasures. Design/methodology/approach This study employs the design science information system research and the design science research process methodologies to ensure the rigor of this analysis. These research methodologies are adopted to tackle identified organisational problems and propose solutions. This methodological approach consists in the following phases: identification of the problems and motivation; definition of the objectives of the solution; research design and development; evaluation; communication. Findings This study detects several implementation challenges for public sector organisations and central banks and proposes adequate solutions. This study finds that these challenges are related to organisations’ complexity, institutional rigidity, potential threats to internal auditors’ independence and the issue of considering CA system as a “real time error correction” mechanism. The solutions involve the development of a business process focussed audit approach to enable internal auditors to analyse CA indicators, and the use of CA systems to support each phase of the audit process. Originality/value This study contributes to the scant strand of literature on internal auditing in central banks. Given the exceptional demand for guidance concerning internal auditing in the public sector and in central banks, this paper provides guidelines for these organisations to implement CA systems and to tackle implementation challenges. The analysis allows internal audit departments within central banks to better support their organisations in the achievement of their important regulatory and policy objectives.

Continuous auditing is a strategic information system that uses data analytic technology, allowing enterprises to automate control examination, risk assessment, and compliance. However, this remains challenging for the internal audit department when adopting the new technology. This paper will discuss a continuous auditing fast adoption methodology in the systems applications products (SAP) enterprise resource planning (ERP) environment. In addition, it will discuss two industry case studies and describe the life cycle of the adoption of the new technology as a lesson learned.  

<div>Efficient change control and configuration management is imperative for addressing the emerging</div><div>security threats in cloud infrastructure. These threats majorly exploit misconfiguration vulnerabilities</div><div>e.g. excessive permissions, disabled logging features and publicly accessible cloud storage buckets.</div><div>Traditional security tools and mechanisms are unable to effectively and continuously track changes in</div><div>cloud infrastructure owing to transience and unpredictability of cloud events. Therefore, novel tools</div><div>that are proactive, agile and continuous are imperative. This paper proposes CSBAuditor, a novel cloud</div><div>security system that continuously monitors cloud infrastructure, to detect malicious activities and</div><div>unauthorized changes. CSBAuditor leverages two concepts: state transition analysis and reconciler</div><div>pattern to overcome the aforementioned security issues. Furthermore, security metrics are used to</div><div>compute severity scores for detected vulnerabilities using a novel scoring system: Cloud Security</div><div>Scoring System. CSBAuditor has been evaluated using various strategies including security chaos</div><div>engineering fault injection strategies on Amazon Web Services (AWS) and Google Cloud Platform</div><div>(GCP). CSBAuditor effectively detects misconfigurations in real-time with a detection rate of over</div><div>98%. Also, the performance overhead is within acceptable limits.</div>

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Research Question: Organizations increasingly buy standardized continuous auditing solutions from vendors rather than develop their own. What opportunities lie in exploring the adoption, implementation and application of such solutions in the context of internal auditing? Motivation: Extant literature has not fully examined the implications of this development and there are several interesting unexplored research questions in this area. Idea: We develop a framework for examining continuous auditing as an information system solution and link this to internal auditing. Data: We employ existing literature as data and build on similar frameworks from ERP systems and AIS research. Findings: The findings of the paper are a series of research questions for examining this relationship as well as a proposal for using different theoretical perspectives and methodologies. Contribution: The contribution is a new perspective on continuous auditing research that could move this research area forward and link it to current developments in the field.