Financial Risk Management Research Articles

Leveraging prompt engineering to enhance financial market integrity and risk management

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.

Evaluating the Impact of Private and Public Sentiments on the Linkage Between Gold and Stock Markets: Evidence from China.

Gold and stocks, which are conventionally regarded as a safe haven and risk assets, respectively, exhibit complex interrelationships, with significant implications for financial risk management. This paper builds on the sentiment categorization proposed by Liang et al. (2020) to distinguish between private and public sector sentiment. The construction of sentiment indices for both sectors aims to allow the exploration of the heterogeneous effects of these sector-specific sentiments on the gold-stock market linkages in China under different market conditions. The empirical results demonstrate a notable asymmetry in the impact of market sentiment between the public and private sectors, with distinct manifestations in stable versus highly volatile market environments. Specifically, positive sentiment in the public sector tends to diminish the safe-haven function of gold, whereas positive sentiment in the private sector tends to reinforce it. This disparity becomes particularly evident during periods of extreme market volatility. Our findings not only underscore the diverse impacts of market sentiment but also provide novel insights into the importance of incorporating sector-specific sentiment when devising hedging strategies for specific industries.

Analytic Hierarchy Process Optimization of Strategic Resource Management for Sustainable Development: Enhancing International Economic and Commercial Relations through Circular Economy Factors in Open Socio-Economic Systems

This article explores how the Analytic Hierarchy Process (AHP) can optimize strategic resource management for sustainable development, emphasizing circular economy factors in open socio-economic systems. A thorough literature review links existing research in financial security, innovation, and risk management to illustrate the significance of integrating environmental considerations into long-term resource allocation. The proposed AHP framework decomposes complex decision-making into a hierarchical structure that includes both quantitative metrics, such as financial stability and waste reduction, and qualitative concerns, like stakeholder engagement and regulatory compliance. Expert evaluations highlight the primacy of environmental and circular economy factors in resource management, closely followed by financial and risk considerations. Waste reduction, resource reuse, and supply chain resilience emerge as top-ranked sub-criteria, underscoring the importance of balancing ecological imperatives with economic viability. The study further highlights digital transformation as an essential enabler of real-time analysis and adaptive resource allocation. Integrating these insights within a broader policy and managerial context offers a roadmap for enhancing international economic and commercial relations, especially in turbulence-prone global markets. The findings suggest that holistic, multi-criteria decision-making—supported by AHP—provides a robust pathway toward sustainable development, promoting resilience and competitiveness in open socio-economic systems.

Study on Financial Risk Prevention of NIO in the Context of Big Data

With the continuous advancement of global informatization, the way of enterprise financial management under the big data environment is undergoing profound changes. This paper takes NIO as the research object to explore how to prevent financial risks in the context of big data. Firstly, the definition of big data and its specific application in financial management are studied to reveal the supportive role of big data technology in the collection, analysis and decision-making of enterprise financial information. Second, this paper provides a detailed definition and categorization of financial risks to help identify potential financial problems. Through in-depth analysis of NIO's financial data, possible financial risk points are identified, and an effective risk early warning model is established by combining the methods of big data analysis, aiming to realize the early identification and prediction of financial risks. Finally, based on the results of the analysis, relevant financial risk prevention measures are proposed, in order to provide reference and reference for NIO and other similar high-tech enterprises in financial risk management. Financial risk management in the context of big data not only requires enterprises to have strong data analysis capabilities, but also needs to eliminate potential risk factors in the system design to enhance the enterprise risk prevention capabilities. The research in this paper is of great practical significance to promote the intelligence and scientization of enterprise financial management.

Integrating asset and liability management with Islamic Finance: A strategic framework for global financial institutions

The integration of Asset and Liability Management (ALM) with Islamic finance presents a strategic opportunity for global financial institutions to optimize their financial stability, capital efficiency, and risk management, while adhering to Shariah principles. This review explores how leveraging Islamic financial instruments, such as Sukuk, Mudarabah, and Murabaha, can enhance traditional ALM frameworks by focusing on ethical investing, risk-sharing, and asset-backed financing. In contrast to conventional ALM, which relies heavily on interest-bearing instruments, Islamic finance mandates compliance with principles that prohibit riba (interest) and excessive uncertainty (gharar), posing unique challenges and opportunities for treasury operations. This is a strategic framework for integrating ALM with Islamic finance, emphasizing liquidity management, capital structure optimization, and risk mitigation. It highlights the role of Islamic capital markets, such as Sukuk issuance, in providing alternative funding sources and promoting sustainable growth. Additionally, the review examines how financial institutions can leverage fintech and digital innovations to streamline Shariah-compliant ALM practices, ensuring efficient liquidity management and compliance with evolving regulatory requirements. Ultimately, this review concludes that integrating ALM with Islamic finance not only supports the growth of Islamic banking but also offers conventional financial institutions a pathway toward ethical and sustainable financial management. By adopting this integrated approach, institutions can achieve long-term stability, optimize returns, and align with the growing demand for socially responsible investing in global markets. Keywords: Integrating Asset, Liability Management Strategic Framework, Global Financial Institutions.

Review of Gen AI Models for Financial Risk Management

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.

Historical Simulation Systematically Underestimates the Expected Shortfall

Expected Shortfall (ES) is a risk measure that is acquiring an increasingly relevant role in financial risk management. In contrast to Value-at-Risk (VaR), ES considers the severity of the potential losses and reflects the benefits of diversification. ES is often calculated using Historical Simulation (HS), i.e., using observed data without further processing into the formula for its calculation. This has advantages like being parameter-free and has been favored by some regulators. However, the usage of HS for calculating ES presents a potentially serious drawback: It strongly depends on the size of the sample of historical data, being typically reasonable sizes similar to the number of trading days in one year. Moreover, this relationship leads to systematic underestimation: the lower the sample size, the lower the ES tends to be. In this letter, we present examples of this phenomenon for representative stocks and bonds, illustrating how the values of the ES and their averages are affected by the number of chosen data points. In addition, we present a method to mitigate the errors in the ES due to a low sample size, which is suitable for both liquid and illiquid financial products. Our analysis is expected to provide financial practitioners with useful insights about the errors made using Historical Simulation in the calculation of the Expected Shortfall. This, together with the method that we propose to reduce the errors due to finite sample size, is expected to help avoid miscalculations of the actual risk of portfolios.

Decoding systemic risks across commodities and emerging market stock markets

This study explores correlations and risk spillovers, essential concepts for financial risk management, among commodities (crude oil, gold, and a global commodities index) and emerging stock markets. Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk (ADCC-CoVaR) model and a bootstrapped Kolmogorov–Smirnov (KS) test, we analyze the period from December 30, 2005, to February 28, 2024, examining correlations, downside and upside risk spillovers, and highlighting the effects of major events such as the global financial crisis of 2008, the COVID-19 pandemic, and the Russia-Ukraine war. The results show heightened correlations during crises and significant risk spillovers across market pairs, with downside risks often outweighing upside risks. Gold displays minimal risk spillover, highlighting its unique role as a haven asset. We find that spillovers between gold, global commodities, and stocks increased during the pandemic and the Russia-Ukraine conflict, while those involving crude oil remained stable. These findings provide valuable guidance for portfolio managers in navigating volatile markets.

Effect of Financial Planning on Financial Performance of Parastatals in Mombasa County Kenya

The objective of this study was to examine the effect of financial planning on the financial performance of parastatals in Mombasa County, Kenya. Specifically, the study focused on key financial planning components, including resource allocation, financial risk management, budgeting, and investment efficiency, and how these factors influenced financial performance, measured through Return on Assets (ROA). Data for the study was collected using secondary data from 16 parastatals operating in Mombasa County, covering a period of 10 years (2013-2022). The population included all the key parastatals in the county, making it a comprehensive analysis of public sector financial planning and performance. The research employed both descriptive statistics and a regression model to analyze the relationship between the financial planning variables and financial performance. The descriptive analysis revealed significant variability in resource allocation, financial risk management, budgeting, and investment efficiency across the parastatals. Regression analysis indicated that resource allocation was the only variable with a statistically significant effects on financial performance, though the relationship was negative. This suggested that inefficiencies in the use of allocated resources may hinder financial growth. Other variables, such as financial risk management, budgeting, and investment efficiency, did not show statistically significant effects on financial performance, implying that these practices might not be effectively contributing to organizational success. The study concluded that financial planning, particularly resource allocation, plays a crucial role in determining the financial performance of parastatals. However, the negative impact of resource allocation suggests that there are inefficiencies in how funds are being managed and allocated within these organizations. Based on the findings, the study recommended that parastatal managers prioritize improving resource allocation efficiency, strengthening financial risk management practices, enhancing budget execution, and improving investment efficiency to boost financial performance. Additionally, policymakers were urged to develop guidelines that promote better financial management and accountability in public organizations. Future research should expand the scope to include more variables and a larger sample size, providing deeper insights into how financial planning can drive better financial outcomes for public sector organizations.

The Role of Financial Risk Management in Improving The Profitability of Commercial Banks: Case Study Saudi France Bank

The aim of this study is to show the impact of financial risk management on the profitability of the Saudi French Bank, and the study relied on the descriptive analytical approach. The study population consisted of employees of the Saudi French Bank and through the method of simple random sample, the number of the sample reached 100 individuals. The study tool was the questionnaire. The results of the study found: the existence of a very high level of risk management in its dimensions (credit risk, liquidity risk, efficiency of the use of funds) in the Saudi French Bank and the high level of profitability in the Saudi French Bank, as well as the presence of a statistically significant impact of financial risk management on the profitability of the Saudi French Bank at the level of significance (a<0.05) and the existence of a direct correlation between the two variables and the value of the correlation coefficient was 0.662 and the hypothesis (H1) was accepted, which states that there is a statistically significant effect of the management of Financial risks on the profitability of the Saudi French Bank It was found that the greater the level of financial risk management by 1%, the greater the level of profitability of the Saudi French Bank by 0.326%. As well as the existence of a statistically significant effect of credit risk management on the profitability of the Saudi French Bank at the level of significance (a<0.05) and the existence of a direct correlation between the two variables and the value of the correlation coefficient 0.506 and the hypothesis (H1) was accepted, which states that there is a statistically significant impact of credit risk management on the profitability of the Saudi French Bank and it was found that the higher the level of credit risk management by 1%, the greater the level of profitability of the Saudi French Bank by 0.658%. It was also found that there was a statistically significant effect of liquidity risk management on the profitability of the Saudi French Bank at the level of significance (a<0.05) and the existence of a direct correlation between the two variables and the value of the correlation coefficient was 0.463 and the hypothesis (H1) was accepted, which states that there is a statistically significant impact of liquidity risk management on the profitability of the Saudi French Bank, and it was found that the greater the level of liquidity risk management by 1%, the greater the level of achieving the profitability of the Saudi French Bank by 0.551%. Based on the results of the study, it recommended the following: the need to work on providing training programs and courses for employees of the Saudi French Bank to increase their capabilities to manage financial risks of all kinds, the need to pay attention to providing all the needs and capabilities through which the highest possible profitability rates can be achieved in the Saudi French Bank, and the need to pay attention to providing the appropriate work environment in all financial institutions in the Kingdom of Saudi Arabia, which contributes to increasing performance levels and achieving the required efficiency and effectiveness. Keywords: Financial risk management – Profitability of commercial banks – Saudi France bank

Evolution of machine learning in financial risk management: A survey

Evolution of machine learning in financial risk management: A survey

Hybrid ML Models for Volatility Prediction in Financial Risk Management

Hybrid ML Models for Volatility Prediction in Financial Risk Management

Causal Inference in Financial Risk Management: Applications of Counterfactual Analysis in Credit Portfolios

Causal Inference in Financial Risk Management: Applications of Counterfactual Analysis in Credit Portfolios

Exploring the Relationship Between Capital Structure and SME Profitability: A Case Study of Lusaka's Electronic Device Market

The capital structure decisions made by Small and Medium Enterprises (SMEs) within the electronic device market in Lusaka, Zambia, significantly impact their financial health and long-term sustainability. This study aims to understand how the allocation of financing through debt and equity affects SME profitability, filling a gap in empirical evidence specific to Zambia's electronic device market. Capital structure decisions play a pivotal role in determining firms' financing policies and overall performance. Through a descriptive research design and panel data analysis, this study investigates the relationship between capital structure and profitability. Findings reveal that while factors such as financial risk, market conditions, and access to financing options may influence SME profitability, the effects are not statistically significant. However, better access to financing options and favorable market conditions are associated with higher growth prospects. Recommendations include exploring additional factors affecting SME growth, adopting a comprehensive approach to financing, conducting market analysis, enhancing financial risk management, capacity building, and government support. Policymakers and SMEs can utilize these insights to optimize capital structure decisions, foster growth, and contribute to economic development in Zambia's electronic device market. Further research, including longitudinal studies, is recommended to deepen understanding and inform strategic decision-making in this dynamic sector.

The Effect of Financial Innovation, Risk Management, and Monetary Policy on the Stability of Fintech Startup Companies in Jakarta

This study investigates the effects of financial innovation, risk management, and monetary policy on the stability of fintech startup companies in Jakarta. Using a quantitative approach, data were collected from 35 fintech startups through structured questionnaires with responses measured on a Likert scale of 1-5. Data analysis was conducted using SPSS version 25, employing correlation and multiple regression analysis. The results reveal that financial innovation is the most significant predictor of fintech stability, followed by risk management and monetary policy. The combined influence of these factors explains 74% of the variance in fintech stability. These findings underscore the importance of integrating innovation with robust risk management practices and aligning operations with macroeconomic trends for sustained stability. This research provides valuable insights for fintech stakeholders and policymakers to foster resilience and growth in the rapidly evolving financial ecosystem.

Conceptual framework for women in compliance: Bridging gender gaps and driving innovation in financial risk management

This paper explores the critical role of gender diversity in compliance and financial risk management, emphasizing the importance of women’s inclusion in shaping innovative and effective strategies. Despite persistent gender gaps in the financial sector, the participation of women has been shown to enhance decision-making, improve risk assessments, and drive organizational innovation. A conceptual framework is proposed, focusing on three key elements: professional development, institutional support, and cultural shifts. This framework provides actionable strategies to empower women in compliance roles, such as leadership training, mentorship programs, and inclusive workplace policies. Additionally, the paper highlights the transformative impact of gender-inclusive practices on compliance strategies, emphasizing how diverse teams are better equipped to identify and manage emerging risks. Concluding with recommendations for regulators, organizations, and educational institutions, this study underscores the urgent need to address gender disparities to foster ethical governance and sustainable growth in the financial sector.

AI-DRIVEN CREDIT RISK MANAGEMENT: A LONGITUDINAL ANALYSIS OF BANKING SECTOR METRICS

The integration of Artificial Intelligence (AI) into financial systems has become a game-changer, particularly in the realm of credit risk management. As banks face increasing pressures to manage risks more effectively, AI technologies offer new avenues for enhancing decision-making processes and improving financial outcomes. This paper explores the transformative impact of AI on credit risk management practices. By analysing key financial metrics—Provision Coverage Ratio (PCR) and Capital Adequacy Ratio (CAR)—across 11 selected commercial banks over a 12-year period, this study provides empirical evidence on how AI adoption has influenced the financial stability and risk management capabilities of these institutions. Utilizing a paired t-test analysis, this research aims to quantify the benefits of AI in mitigating credit risks and enhancing the overall resilience of banks, offering valuable insights for both practitioners and scholars in the field of financial management.

Matrix-Variate Value-at-Risk: Generalized Beta and F Distributions

In recent years, there has been a notable increase in the study of matrix-variate distributions and their applications. Significant progress has been made in understanding the properties and statistical inference of these distributions. In this paper, we introduce two alternative extensions of the univariate Value-at-Risk (VaR) within a matrix-variate context: the matrix upper VaR and the matrix lower VaR. These extensions are obtained as the zeroes of the Gauss hypergeometric function with a matrix argument, thereby providing valuable tools for risk assessment in a variety of fields, particularly in finance and capital allocation. In this paper, we provide the univariate VaR for the generalized beta and F distributions, as well as the matrix-variate VaR for these distributions. Moreover, we derive the beta-Kotz VaR based on a general family of distributions, which includes the classical Gaussian model. Furthermore, new integrals and results involving zonal polynomials are derived. This paper advances the understanding of matrix-variate VaR extensions, opening new avenues for their application in various disciplines. By bridging the gap between matrix-variate distributions and VaR, we aim to stimulate further research and practical implementations in financial risk management and capital optimization.

The Impact of Sentiment on Realized Higher-Order Moments in the S&P 500: Evidence from the Fear and Greed Index

This study empirically investigates the relationship between realized higher-order moments and the Fear and Greed Index as a measure of sentiments. We estimate daily realized moments using 5 min return data of the S&P 500 index from 3 January 2011 to 18 September 2020. We find that the Fear and Greed Index significantly impacts realized volatility during periods of extreme fear. Additionally, various sentiment indicators influence realized skewness and realized kurtosis. The VIX index significantly reduces realized skewness across all sentiment levels. Bearish and bullish sentiments have a significant negative relationship with negative realized skewness during periods of extreme fear and extreme greed. However, the Fear and Greed Index and bearish and bullish sentiments have a significant positive relationship with positive realized skewness. During extreme fear, the Fear and Greed Index and bearish and bullish sentiments have a significant negative relationship with realized kurtosis. These results remain consistent when considering the non-linear characteristics of the Fear and Greed Index during periods of extreme fear and extreme greed. These findings highlight the relevance of understanding sentiment in financial risk management and its significant relationship with the asymmetric and extremity characteristics of asset returns.

Financial Risk Management and Countermeasure Research in Commercial Banks: A Case Study of Bank X

In the context of global economic integration and rapid progress of financial technology, commercial banks are facing unprecedented financial risk challenges. Taking Bank X as the research object, this paper analyzes in depth the causes of its financial risks and proposes corresponding management countermeasures. Using a combination of quantitative analysis and case studies, the paper identifies risks and proposes solutions by comparing the key financial indicators of Bank X with the industry average. It is found that Bank X has significant risks in terms of capital structure, liquidity, profitability and management operations, which mainly stem from factors such as intensified market competition, irrational asset-liability structure and inadequate internal control. The unique value of this paper is that it provides targeted risk management strategies for regional city commercial banks.