Artificial Intelligence for Financial Risk Management and Analysis

Artificial Intelligence (AI) is playing an increasingly vital role in the field of financial statement analysis. AI methodologies, such as Deep Learning, Machine Learning, and Natural Language Processing, are being employed to enhance the analysis of financial data. This study explores the applications of AI in financial statement analysis, including Predictive Analytics, Anomaly Detection, Trend Analysis, Financial Report Automation, Risk Evaluation and Control, Comparative Analysis and Benchmarking, Portfolio Optimization, Risk Assessment and Management, Fraud Detection and Forensic Analysis, and Regulatory Reporting and Compliance. The chapter includes case studies, industrial insights, and practical applications of AI in financial statement analysis, highlighting the tangible benefits and opportunities for businesses in the finance sector. Additionally, the chapter addresses the challenges and potential future applications of AI in this domain.

The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI and large language models (LLMs), has ushered in a new era of possibilities for the financial sector. This paper explores the integration of these cutting-edge technologies into financial sector risk management, examining both the potential applications and the necessary regulatory frameworks. We provide a comprehensive analysis of how generative AI and LLMs can revolutionize risk assessment, fraud detection, market analysis, and regulatory compliance. The study delves into the technical aspects of these AI models, their implementation challenges, and the implications for existing risk management practices. Furthermore, we propose a novel framework for the responsible adoption of AI in financial risk management, addressing concerns related to model interpretability, data privacy, and algorithmic bias. Our findings suggest that while generative AI and LLMs offer unprecedented opportunities for enhancing risk management capabilities, they also necessitate a recalibration of regulatory approaches to ensure financial stability and consumer protection. This research contributes to the growing body of literature on AI in finance and provides actionable insights for practitioners, policymakers, and researchers in the field.

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS)(NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translationAI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysisCybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network securitySocial good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

The author of the article considers the main directions of application of artificial intelligence in financial management. The article reviews the main scientific sources and positions of practitioners on the strategic prospects of applying artificial intelligence tools in financial management. The research is based on the methodology of systematic review and synthesis of scientific sources and industry reports, which is the most justified for this area of ​​research. The article analyzes key indicators of the artificial intelligence market in the financial sector according to data from leading analytical companies, which demonstrates the impressive growth dynamics of the global artificial intelligence market by 2030-2034. The author systematizes the main challenges faced by the implementation of artificial intelligence in financial management, such as: ethical and regulatory lags, scaling problems, environmental impact and data bias. Based on the analysis of market trends, the evolutionary path of using artificial intelligence tools has been determined, which indicates the growth of market maturity, which is moving from the stage of "technology for the sake of technology" to the stage of "solutions for the sake of business tasks". The author identified strategic vectors for the implementation of artificial intelligence technology in financial management, among which three main ones were conceptually identified: increasing operational efficiency, risk management and cybersecurity, personalization and customer experience. Specific areas of application in which the implementation of artificial intelligence has the most significant results in financial management have also been systematized: fraud detection, creditworthiness assessment, forecasting needs, democratization of financial services, development of virtual assistants, etc. The article pays attention to comparing the main models of generative artificial intelligence and their possibilities of use as tools in financial management, in particular, advantages, limitations and the most optimal type of use.

In the past, financial risk management has always faced a changing market environment and while there were several traditional predictive models available for use, they have limited capabilities in addressing non-linear or high-dimensional risk factors. Therefore, enhancing the foresight in risk identification as well as the accuracy of decision-making within financial institutions will require further exploration into the application value of artificial intelligence and big data technology. Consequently, the goal of this paper is to conduct a comprehensive evaluation regarding the integration pathways and effectiveness of implementing these two technologies into financial risk predicting and managing practices. Findings from the research indicate that AI algorithms offer a deeper understanding of complex, hidden relationships among large amounts of multiple sources of information, while the availability of big data platforms provides opportunities for continuously monitoring risk in real-time. The combination of the two not only significantly improves the timeliness and accuracy of risk prediction, but also promotes the intelligent transformation of risk management models from passive response to active intervention.

Artificial Intelligence (AI) is increasingly recognized as a transformative force driving sus-tainable business innovation in the financial sector. This study conducts a methodological meta-analysis of existing research to examine AI’s role in advan-cing sustainable finance. By systematically reviewing and synthesizing literature from peer-reviewed journals, industry reports, and academic sources, this study focuses on AI applications such as machine learn-ing and neural networks that support environmental, social, and governance (ESG) objec-tives. Key applications include AI-driven financial forecasting, risk management, and auto-mated reporting systems that enhance transparency and facilitate green finance initiatives. Each selected study was rigorously evaluated for methodological quality and relevance to ensure robust findings. The analysis identifies recurring themes, challenges, and gaps in the current literature, with an emphasis on ethical considerations and regula-tory compliance. The study provides insights into how AI can improve decision-making processes by integrat-ing sustainability indicators, thus fostering long-term value creation in finance. The findings underscore AI’s strategic importance in achieving sustainability goals and offer a foundation for future research and inno-vation in sustainable finance. JEL-codes: O33, C18, G21, Q01, D83

This research identifies trends in artificial intelligence (AI) in auditing and fraud detection using a combination of two methods: a bibliometric and a systematic review of AI trends in auditing in fraud detection. This research develops a bibliometric analysis of 1,348 papers on “fraud”, “auditing”, and “artificial intelligence” from 1986 to 2022. The results provide a robust set of information for in-depth research on AI trends in auditing and security detection. They not only demonstrate that there is growing academic interest in the research topic of fraud but also show clear evidence that the words “fraud”, “crime”, and “fraud detection” were the most cited, generating a great impact in the literature and developing concern with the topic. Our analysis suggests that the application of AI allows for greater facilitation of procedures to combat fraud and irregularities in the field of criminal justice and fundamental rights. Most technological changes increase ethical motivations to deter fraud, and these changes will lead to a long-term decrease in the incidence of fraud (Karpoff, 2021). This research contributes to AI valuing in audit procedures to detect and prevent fraud and simultaneously mitigate it. It also contributes to the literature, highlighting trends in AI, auditing and fraud detection, thereby enabling the development of professional judgment on the topic and providing direction for future investigations.

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

The rapid digitalisation of the Indian banking sector has revolutionised financial services, improving efficiency, accessibility, and customer experience. However, this transformation has also heightened the vulnerability of banks to sophisticated fraud, cybercrime, and financial misconduct. Conventional fraud detection techniques, often rule-based, struggle to keep pace with the evolving threat landscape. Consequently, artificial intelligence (AI)-powered fraud detection systems have emerged as a strategic solution to protect financial institutions and their customers. This paper explores the application of AI in fraud detection and risk management within the Indian banking system, analysing its impact on financial security, operational efficiency, and customer trust. It highlights how machine learning, natural language processing, predictive analytics, and behavioural biometrics are being deployed to detect anomalies, identify suspicious patterns, and prevent fraud in real time. Additionally, the study examines how AI enhances compliance with regulatory frameworks, particularly in areas such as anti-money laundering (AML) and Know Your Customer (KYC) obligations. Using secondary data sources, including research articles, industry reports, and case studies, this paper evaluates both the benefits and challenges of integrating AI into fraud detection frameworks. Findings suggest that while AI significantly reduces false positives, enhances scalability, and strengthens proactive fraud management, issues such as data privacy, model transparency, and infrastructural readiness remain pressing concerns. The research concludes that AI-powered fraud detection is not merely an optional innovation but a necessity for Indian banks to safeguard financial systems in an increasingly digital economy. Practical recommendations include fostering collaboration between banks and fintech firms, investing in AI research and training, adopting explainable AI models for regulatory trust, and strengthening cybersecurity awareness among customers. By leveraging AI responsibly and effectively, Indian banking institutions can build resilient systems that balance security with innovation, thereby enhancing long-term sustainability and public trust.

Type 2 diabetes mellitus (T2DM) poses a significant global public health challenge, with its prevalence escalating continuously and disproportionately affecting low- and middle-income countries (LMICs), imposing a substantial burden on healthcare systems. Traditional management models have limitations in disease prediction, personalized treatment, and public health intervention. Artificial intelligence (AI) and digital health technologies provide novel insights for precise prediction and intelligent management of T2DM. This review systematically summarizes research progress in AI's role in deciphering T2DM pathogenesis, personalized treatment, and public health management. By integrating multi-omics and environmental data, AI reveals key mechanisms including gene-environment (G × E) interactions, β-cell dysfunction, and inflammatory pathways, significantly enhancing early screening and risk prediction. In clinical management, AI combined with digital health tools [e.g., continuous glucose monitoring (CGM), wearable devices, and mobile health (mHealth) apps] facilitates remote monitoring, medication optimization, and personalized interventions, improving treatment adherence and health management efficiency. At the public health level, AI optimizes resource allocation and disease burden assessment, promoting chronic disease prevention and control model transformation. Future efforts should prioritize developing low-resource-adapted tools, strengthening data privacy protection tailored to LMICs, and addressing algorithmic fairness and the digital divide to ensure safe, equitable, and sustainable AI application in global T2DM management. Overall, AI and digital health integration is driving T2DM management towards an intelligent and precision-based era, with the potential to reduce disparities in LMICs.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

Purpose The main objective of this study is to analyse the barriers that limit the adoption of artificial intelligence (AI) in SMEs. To do so, seven risk categories will be examined: functional (FUR), hedonic (HR), security (SR), social (SOR), financial (FR), performance (PR) and psychological (PYR). The choice of these seven categories is based on a comprehensive review of the literature on technology adoption and perceived risks, and these categories are considered to comprehensively capture the main risks associated with AI as perceived by SMEs. Additionally, the influence of factors such as technostress (TE), distrust (DT) and hesitancy (HES) on the adoption process will be investigated. Design/methodology/approach This study offers a contribution to the analysis of barriers limiting the adoption of AI in small and medium-sized enterprises (SMEs), addressing the problem from a dual methodological approach integrating PLS-SEM and fsQCA techniques. Seven categories of perceived risks (functional, hedonic, security, social, financial, performance and psychological) along with psychological and organisational factors such as technostress, distrust and hesitation were investigated using a representative sample of 395 SMEs. This dual methodological approach allowed us to identify not only significant causal relationships but also complex configurations that determine technological adoption. Findings The results reveal that distrust is a decisive factor affecting the intention not to adopt AI. This finding highlights the importance of creating strategies that increase trust in automated processes. Functional risk and hedonic risk were also identified as significant barriers, highlighting how perceived usefulness and subjective experience of pleasure influence willingness towards technology adoption. In contrast, financial risk and performance risk were not significant in this research, suggesting that SMEs may be more driven by factors related to knowledge and familiarity with technologies than by strictly economic or performance concerns. These findings challenge previous perceptions and open new perspectives to explore how organisational capabilities modulate technology decisions. Research limitations/implications The result underlines the need for initiatives that reduce hesitation and effectively communicate the benefits of AI, especially in key sectors of the economy. Taken together, these findings are fundamental for the design of public policies and business strategies aimed at fostering digital transformation in this strategic sector. Practical implications In terms of managerial implications, the adoption of AI by companies must first face the process of hesitation and distrust. The implementation of these processes must be clearly and accurately disseminated within the business structure, going through a process of proper understanding by managers. Any attempt that does not entail adequate knowledge or that generates a certain degree of hesitation may constitute a major failure that, through an inadequate strategy, will end up affecting the client. Companies providing AI services must develop a comprehensive strategy of enrichment and enhancement of the product, determining not only the advantages of its application in business strategies but also the positive effects on the company's value proposition. Social implications Differences in the perception of risks, such as financial, functional and psychological risks, may intensify the technology gap between different social groups, especially those with different levels of access to technology education. This could lead to significant disparities in how different sectors of society benefit from AI applications, limiting their inclusive reach and positive impact on the general population. Moreover, there are growing concerns about the impact of AI on employment and labour skills, as the perception that this technology could lead to dependency or replace human functions negatively influences its uptake. Originality/value This study represents one of the first investigations to comprehensively address the risks associated with the use of AI by firms, and to address the level of direct risk that this represents with respect to their behaviour. The treatment of non-adoption issues is an innovation in the academic approach, allowing for more comprehensive conceptual modelling of findings. This study provides a theoretical basis for a comprehensive analysis of the risks affecting the adoption of AI in firms, marking an academic innovation by focussing the analysis on non-adoption behaviour.

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

This study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets. Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry.