Financial Risk Management Research Articles

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

Corporate Social Responsibility and Country Governance: An International Comparative Study Amid the COVID-19 Pandemic

This paper assesses the association of ESG scores with stock returns and highlights the moderating role of the COVID-19 pandemic and the country’s governance. The study uses panel data regression models to assess the relationship between ESG factors and stock returns, focusing on the moderating role of country governance and the COVID-19 pandemic. The results reveal that governance quality significantly enhances the positive effects of ESG practices on returns, particularly during times of crisis. These suggest that higher overall ESG scores are related positively to financial performance, and this relation is enhanced during the COVID-19 pandemic. Specifically, the two dimensions of ESG that matter most are environmental and governance. Country-level governance is important because firms in well-governed countries amplify the benefits of high ESG scores. The opposite is true for the higher controversies scores, whose bad financial outcome is magnified during the pandemic. These results present an argument for the resilience of firm financial performance, dependent on strong ESG practices and governance frameworks. This holds great interest for investors and policymakers in associating good ESG considerations with the effective management of financial risks, leading to sustainable returns during periods of widespread economic uncertainty.

The Financial Risks in Toll Road Development in Indonesia: Challenges and Development Strategies for the Probowangi Toll Road

Probolinggo-Banyuwangi toll road, which crosses mountainous areas in East Java. Toll road construction in areas with varying topography often faces significant technical and environmental challenges, which can affect costs, completion time, and project sustainability. This study uses a quantitative approach with a descriptive method to evaluate risk factors that impact project finances, such as construction costs, dependence on funding sources, interest rate fluctuations, and traffic volume predictions. Primary data was collected through questionnaires, while secondary data was obtained from financial planning documents and related project topography. Data analysis was conducted using multiple linear regression with a path analysis model. The results of this study are expected to identify key variables that influence financial risk and provide recommendations for effective mitigation strategies. This research is expected to contribute to the development of policies and financial risk management practices for similar infrastructure projects in the future.

Building a theoretical framework for financial risk management in emerging markets: Applying global best practices to the oil and gas industry in developing economies

Financial risk management is crucial for the sustainability and growth of the oil and gas industry in developing economies, where volatility and regulatory uncertainties present significant challenges. This abstract explores the construction of a theoretical framework for financial risk management, adapting global best practices to the specific contexts of emerging markets within the oil and gas sector. In developing economies, the oil and gas industry faces unique risks such as geopolitical instability, currency fluctuations, and regulatory changes. Effective financial risk management mitigates these risks through proactive strategies that balance risk exposure with strategic objectives. Adopting global best practices involves the integration of comprehensive risk assessment methodologies, scenario planning, and hedging techniques tailored to local market conditions. Key components of the theoretical framework include risk identification through comprehensive analysis of market, operational, and financial factors. This approach enables oil and gas companies to anticipate and mitigate risks before they escalate, ensuring financial stability and resilience in volatile environments. Furthermore, the application of global best practices in financial risk management fosters transparency and accountability, enhancing investor confidence and facilitating access to capital. By aligning risk management strategies with international standards and benchmarks, developing economy oil and gas firms can navigate uncertainty more effectively, thereby optimizing operational performance and sustaining long-term growth. However, challenges such as limited access to sophisticated financial instruments and expertise may hinder effective implementation. Overcoming these barriers requires capacity building, collaboration with experienced partners, and continuous adaptation of strategies to evolving market dynamics. In conclusion, building a theoretical framework for financial risk management in the oil and gas industry in developing economies is essential for mitigating volatility and maximizing opportunities. By applying global best practices tailored to local contexts, companies can strengthen their resilience, attract investment, and drive sustainable development in emerging markets.

Research on the Application of Blockchain Technology in Financial Risk Management

This paper explores the application of blockchain technology in financial risk management. By analyzing foundational blockchain concepts such as public and private chains, smart contracts, and consensus mechanisms, the paper first illustrates how blockchain is transforming financial risk management practices. Additionally, it highlights challenges related to scalability, interoperability, and regulatory/legal barriers. Finally, the paper discusses innovative opportunities that blockchain presents for financial risk management, aiming to provide financial institutions with new strategies and insights to tackle modern financial challenges.

Risk Management in Internet Consumer Finance: A Case Study of Douyin Monthly Pay

Abstract: This study aims to explore risk management in internet consumer finance, taking Douyin Monthly Pay as a case study for in-depth analysis. Risk management is particularly crucial with the rapid development of internet consumer finance. By studying the current situation of risk management in Douyin Monthly Pay, some problems and challenges can be found. This study puts forward some suggestions and conclusions through detailed case analysis. Through this research, the risk management practice in Internet consumer finance can be better understood, providing valuable references and insights for related enterprises. Given the current context of internet consumer finance development, the importance of risk management is self-evident. Through the Douyin Monthly Pay case study, specific operations and risk management challenges can be thoroughly understood, guiding the industry toward future development. The conclusions and recommendations of this study can offer certain references and insights for relevant enterprises in risk management, promoting the healthy development of the industry.

A Study on Enterprise Risk Management in the Chinese Context

Abstract: The establishment of the market economy, accompanied by the emergence of risks, requires enterprises to consider not only the economy and profitability but also the risk. Therefore, enterprise risk management problems arise. Many developed countries see risk management, strategic management, and operation management as the three main management functions of modern economic organizations, which will become a critical factor in the competitiveness of businesses in the future. Therefore, the paper reviews the development of traditional risk management theory, financial risk management theory, and internal control theory, with a focus on Enterprise Risk Management (ERM). After analyzing the current situation of ERM in Chinese enterprises, the paper proposes suggestions for improving risk management methods, strengthening the internal control system, and enhancing staff quality. Finally, the paper emphasizes the importance of Enterprise Risk Management (ERM) development in China and suggests the need for a risk management system tailored to the countrys specific circumstances.

Effect of Credit Risk Management Techniques on Financial Performance of Deposit Taking SACCOs in Kenya

Financial performance is crucial in the realm of finance. The requirement to explain why two organizations operating in the same environment perform differently is a puzzle, and various financial research works have been devoted to solving this puzzle. Co-operative societies in Kenya are recognized as a significant contributor to national development since their presence can be traced in virtually all sectors of the economy. Although significant progress has been made by the deposit taking SACCOs in Kenya, their performance and sustainability has been debatable. The objective of this research was to assess the effect of financial risk management techniques on financial performance of deposit taking SACCOs in Kenya. The specific objective was to determine the effect of credit risk management techniques on financial performance of deposit taking SACCOs in Kenya. The study was based on Credit Risk Theory. A descriptive survey design was adopted. The population of the study was the 175-deposit-taking SACCOs in Kenya as of 31st December 2022. Due to relative population nature, sampling was not conducted and therefore the study was a census. The respondent was the risk manager of each deposit-taking SACCO. The study utilized primary data. The data was collected using a structured questionnaire. Data was analyzed using descriptive statistics such as the mean and standard deviation and inferential statistics which included correlation and regression analysis. Reliability tests were conducted on the variables each meeting the 0.7reliability threshold with validity tests being confirmed by the Bartlett’s test that confirmed validity of the variables being measured. The regression analysis revealed significant insights into the relationship between financial risk management techniques and the financial performance of deposit-taking SACCOs in Kenya. Credit risk management techniques were found to have a positive impact on financial performance with a regression coefficient of β = 0.238 and a significant value of P <0.005. In conclusion, the study provides compelling evidence of the importance of comprehensive risk management frameworks in driving the financial performance of deposit-taking SACCOs in Kenya. By implementing effective risk management practices tailored to address credit risk, SACCOs can enhance their stability, profitability, and resilience in a dynamic operating environment. These findings have significant implications for policymakers, practitioners, and stakeholders in the SACCO sector, highlighting the need to prioritize investments in risk management capabilities to promote sustainable financial growth. The study recommended that SACCOs should focus on enhancing their credit risk management frameworks. This can be achieved by implementing robust credit risk assessment mechanisms, such as comprehensive credit scoring models and regular analysis of customer credit histories.

GESTÃO DE RECURSOS FINANCEIROS NO SETOR PÚBLICO

This work aims to analyze the management of financial resources in the public sector, highlighting the importance of efficient, transparent, and responsible administration. The research explores the particularities of management in sectors such as health, education, and security, while also addressing strategies for financial risk management and the use of information technologies to improve transparency and efficiency. Based on a bibliographic review and interviews with public managers, the goal is to propose strategies that can contribute to maximizing the positive impact of governmental financial resources, promoting social well-being and sustainable development.

Impact of Debt Composition and Accounting Conservatism on Financial Distress in Emerging Markets

Aims: This study investigates the impact of debt composition, specifically short-term and long-term debt, on corporate financial distress, focusing on the moderating role of accounting conservatism. It aims to understand how firms in emerging markets, particularly Kenya, can achieve financial stability through prudent debt structuring and conservative accounting practices. Study Design: The research adopted an explanatory research design. Place and Duration of Study: The study utilized financial data from firms listed on the Kenyan Securities Exchange between 2008 and 2021. Methodology: The study analyzed a sample of 45 firms trading at the Nairobi Stock Exchange over 14 years from 2008 to 2021. The study used panel logistic regression, a statistical method for analyzing data with multiple observations over time, to test its hypotheses. Results: The results show that both short-term and long-term debt increase the likelihood of financial distress, with accounting conservatism further intensifying the risk for firms heavily reliant on short-term debt while mitigating it for those with long-term debt. This suggests that conservative accounting practices, which promote early recognition of losses, may exacerbate financial distress for firms heavily reliant on short-term debt. In contrast, for long-term debt, accounting conservatism has a mitigating effect helping firms manage their debt obligations more effectively over time. This highlights a nuanced role of accounting conservatism, where it increases the pressure on firms with short-term debt while providing stability for those with long-term obligations. Conclusion: The findings suggest that corporate managers in emerging markets should carefully balance short- and long-term debt to minimize financial distress risks. Additionally, adopting conservative accounting practices can serve as a safeguard, providing early warning signals for financial trouble and enhancing corporate stability. Managers should therefore carefully balance short-term and long-term debt to reduce financial distress while adopting conservative accounting practices to enhance financial transparency and risk management. Policymakers can support firms by promoting responsible debt management and offering incentives for conservative financial reporting to strengthen overall financial stability.

Exploring AI's Role in Enhancing Risk Assessment Models in Financial Quantitative Trading

The application of Artificial Intelligence (AI) algorithms to financial risk management has fundamentally transformed the nature of risk and the tools being used to control and manage it. This paper examines how AI is deployed to manage market risk, including volatility forecasting, portfolio optimisation, stress testing, and sentiment analysis. While many of these models are well-accepted across the industry and underpin many front-office and risk-management processes, they are often limited in dynamic market environments where traditional linearity doesnt exist and real-time data is king. AI has brought advanced tools that can be applied to large and complex data sets, and that adapt quickly as market conditions change. Reinforcement learning algorithms are now deployed to optimise portfolio weights dynamically. Natural-language processing is also used to augment sentiment analysis, which can be leveraged to assess potential risk. Other forms of AI can be deployed to address challenges that come with more advanced AI models. Explainable AI models provide the opportunity to produce financial outcomes that are more transparent and less opaque while also helping to validate regulatory compliance. Analyzing the applications of AI case studies sheds light on why financial professionals must continue to embrace AI tools, many of which are designed to help them manage risk in an increasingly volatile market environment.

Managing Financial Risks of Global Companies Through Corporate Social Responsibility: The Specifics of Sustainable Employment in Developed and Developing Countries

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune “Global 500” rankings for 2021–2023. As a result, the consequences of global companies’ CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

EXPLORING THE ROLE OF ARTIFICIAL INTELLIGENCE IN MANAGING EMERGING RISKS: AN IN-DEPTH STUDY OF AI APPLICATIONS IN FINANCIAL INSTITUTIONS' RISK FRAMEWORKS

This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

Financial Analysis of Real Estate Companies Based on the Harvard Framework: A Case Study of Poly Developments and Holdings

China's real estate market saw considerable volatility between 2019 and 2023. Poly Developments and Holdings Group Co., Ltd. successfully weathered the market downturn of 2020, the rebound in 2021, and the subsequent downturn in 2022 to retain its position as one of China's top real estate companies, which has peaked by 2024. Using the Harvard Analytical Framework, this paper covers strategic, accounting, financial, and prospective aspects of Poly Developments over the past five years, conducting a comprehensive financial analysis. By regularly implementing outstanding financial management strategies, Poly Developments clearly shows that it effectively controlled important market volatility over the past five years. Poly Developments achieved exceptional performance by adopting financial methods that were well-suited to its specific circumstances, avoiding excessive aggression during market upswings and excessive caution during downturns. In addition, the study suggests that Poly Developments should boost its financial risk management, grow its market, improve cost control and operational efficiency, and strengthen asset usage and inventory management.

Revolutionizing Inventory Management: The Role of Data Mining in Industry 4.0

In the era of Industry 4.0, with the rapid development and application of technologies such as the Internet of Things (IoT), Big Data and Artificial Intelligence (AI), the manufacturing industry is undergoing unprecedented changes. In this context, data mining technology has become integral to inventory management practices in all industries. This study examines the profound impact of data mining on inventory management efficiency. By leveraging advanced analytics and machine learning algorithms, data mining enables organizations to accurately forecast demand, optimize inventory levels, and improve supply chain transparency. Through real-world case studies and comprehensive analysis, this research highlights how data mining techniques, such as central object-based clustering algorithms, can be successfully applied to optimize material classification, warehouse space utilization and operational efficiency. In addition, this study explores the broader applications of data mining beyond inventory management, including marketing, financial risk management, healthcare, transportation, social media, and cybersecurity. Overall, this study provides valuable insights into how data mining can reshape inventory management practices and drive business growth in the digital age.

The Application of Fintech (Financial Technology) and Risk Management

This paper provides a comprehensive overview of the impact and applications of financial technology (Fintech) across various sectors such as payment and clearing, lending, wealth management, insurance, and risk management. It highlights the transformative potential of Fintech in enhancing financial efficiency, inclusiveness, and risk management through the utilization of modern technologies like big data, artificial intelligence, and blockchain. The paper delves into specific examples such as online payment systems, peer-to-peer lending platforms, robo-advisory services, and blockchain technology. Furthermore, it discusses the challenges and risks associated with Fintech, including regulatory issues, security concerns, and the need for effective risk management practices. The abstract emphasizes the importance of responsible risk management in ensuring financial stability and preventing systemic risks in the Fintech sector. Overall, the abstract provides a comprehensive understanding of the role of Fintech in shaping the future of the financial industry and emphasizes the need for effective regulation, risk management, and technological innovation to harness its full potential while mitigating associated risks.

Distortion Risk Measures of Increasing Rearrangement

Increasing rearrangement is a rewarding instrument in financial risk management. In practice, risks must be managed from different perspectives. A common example is the portfolio risk, which often can be seen from at least two perspectives: market value and book value. Different perspectives with different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as an increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce increasing rearrangement for application in financial risk management and to apply increasing rearrangement to the class of distortion risk measures. We derive formulae to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely the value at risk, expected shortfall, range value at risk, conditional value at risk, and Wang’s risk measure. Finally, we present the connection of increasing rearrangement with inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as an illustrative example of the method.

Unveiling the role of city commercial banks in influencing land financialization: Evidence from China

Local financial development is crucial for advancing regional financial supply side structural reform, enabling local governments to leverage financial instruments to effectively mobilize land resources and foster competitive growth. The introduction of numerous financial products linked to land-related rights and interests has resulted in a pronounced transmission and interconnection of fiscal and financial risks across regions. This study examines the impact of local financial development on land financialization in China using panel data from prefecture-level cities and detailed information on land mortgages. The findings indicate that the establishment of city commercial banks (CCBs) contributes to the progress of land financialization by incentivizing local government financing vehicles to participate in land mortgage financing, increasing the transfer of debt risks to the financial sector. Notably, the impact of CCBs on land financialization is more pronounced in regions with urban agglomeration, high GDP manipulation, inadequate local financial regulation, and robust implicit government guarantees. Further analysis reveals that CCB establishment has negative spillover effects on land financialization in neighboring areas, while expansion strategies such as establishing intercity branches, engaging in cross-regional mergers, and relaxing regulations have mitigated the rise of land financialization at the regional level. This study provides policy recommendations that focus on reducing local governments’ reliance on land financing and enhancing the prevention and management of financial risks.

New control variates for pricing basket options

Abstract Accepted by: Giorgio Consigli Accurate pricing of basket options, which are financial derivatives on multiple underlying assets, is a challenging and practically important task for financial institutions. We propose several new control variates for accurate, fast and efficient pricing of basket options. The first approach to deriving new control variates is the use of Hermite polynomial approximation of appropriate function of the underlying asset prices, which leads to a Black–Scholes-like analytic solution. This approach is new in the option pricing context and opens up new possibilities in derivative pricing. Further control variates are analytically derived using Jensen’s inequality in one case, and distributional properties of multivariate Wiener processes in other cases. All the newly proposed control variates are shown to lead to excellent variance reduction in numerical experiments based on realistic data. The proposed methods are novel, computationally simple and have a strong potential to replace more conventional methods, such as the geometric lower bound in simulation-based pricing of basket options and similar products used in financial risk management.