Risk Of Financial Institutions Research Articles

The Role of Artificial Intelligence in Investment Decision-Making: Opportunities and Risks for Financial Institutions

The Role of Artificial Intelligence in Investment Decision-Making: Opportunities and Risks for Financial Institutions

Generation mechanism of behavioral risk for organizational decision-makers in financial institutions: organizational and human errors

This study investigates the mechanism behind the generation of behavioral risk among decision-makers in financial institutions. We start by examining the general mechanism of organizational decision-making behavior, then analyse organizational errors and human errors in the decision-making process, and subsequently define the connotations and types of organizational decision-makers’ behavioral risk. The study also identifies the necessary conditions for the emergence of decision-making behavioral risk. We construct a model to analyse the behavioral risk of decision-makers in financial institutions. We also develop motivation and utility functions for organizational decision-makers’ behavior, examine the relationship between factors in the utility function, and explore how organizational decision-makers adjust their behavior in various situations to maximize utility. The paper also analyses a case study involving the China Everbright Group (CEG). The study revealed the following: ① The conditions for the occurrence of organizational decision-makers’ behavioral risk in financial institutions mainly stem from the conflict between organizational interests and decision-makers’ interests, the failure of organizational contextual constraints, and the lack of organizational decision-making auditing and feedback mechanisms. ② Organizational decision-makers tend to exhibit inappropriate decision-making behaviors when they overestimate their own abilities in comparison to the benefits they expect. ③ Financial institutions should avoid organizational errors to reduce the likelihood of behavioral risk by increasing the cost of organizational decision-makers’ behavior. Our study proposes measures to mitigate the risk of inappropriate behavior by financial institutions through scientific decision-making.

Survey of Fraud Detection Techniques for Credit Card Transactions

With the rapid advancement of Internet technology in recent years, online financial transactions have become increasingly popular for purchasing a wide range of goods and services over the Internet, owing to their numerous advantages. The widespread adoption of credit cards has thus heightened the potential for abuse. Present a significant menace to users as a result of pervasive fraudulent operations. Credit card fraud has become an essential concern in today's digital era, posing substantial financial losses and security risks for financial institutions and consumers. In response to this growing challenge, researchers and industry experts have continuously developed and refined fraud detection techniques to safeguard against fraudulent activities. This survey paper aims to provide a comprehensive overview of the latest advances in fraud detection techniques for credit card transactions. Explore various methodologies, data sources, and machine learning algorithms used in fraud detection systems. Additionally, they discuss the challenges these systems face.

Effective credit risk mitigation strategies: Solutions for reducing exposure in financial institutions

Credit risk remains a critical concern for financial institutions, especially in the context of economic uncertainties and volatile market conditions. This paper examines the importance of credit risk mitigation strategies and presents solutions for reducing exposure in financial institutions. The research focuses on various strategies employed by banks and other financial institutions to manage credit risk effectively. The paper begins with an overview of credit risk and its significance in the banking industry. It discusses the various types of credit risk faced by financial institutions, including default risk, concentration risk, and systemic risk. The paper then highlights the importance of credit risk mitigation strategies in reducing the impact of these risks on financial institutions' balance sheets and profitability. Next, the paper explores different credit risk mitigation strategies used by financial institutions. These strategies include diversification of credit portfolios, collateralization of loans, credit derivatives, and credit insurance. The paper discusses the advantages and disadvantages of each strategy and provides examples of how these strategies have been implemented successfully in the banking industry. Furthermore, the paper examines the role of regulatory authorities in overseeing credit risk management practices in financial institutions. It discusses the regulatory framework for credit risk management and highlights the importance of compliance with regulatory requirements in mitigating credit risk. Finally, the paper concludes with a discussion on the future of credit risk mitigation strategies in financial institutions. It emphasizes the need for continuous innovation and adaptation to new market conditions and regulatory requirements. The paper also stresses the importance of integrating credit risk management into overall risk management frameworks to ensure a comprehensive and effective approach to managing risk in financial institutions. Overall, this paper provides valuable insights into the importance of credit risk mitigation strategies in financial institutions and offers practical solutions for reducing exposure to credit risk. It serves as a useful resource for financial professionals, regulators, and policymakers seeking to enhance their understanding of credit risk management practices in the banking industry.

Financial stability and European union allowances: a tale of jumps and regime shifts

ABSTRACT Using an event-study methodology, we investigate the impact of European Union Allowances (EUA) on measures of systemic risks for financial institutions. We analyse daily data on all banks listed on European exchanges. We find statistically significant abnormal changes in systemic risk measures around days when prices of allowances either jumped or shifted regimes. The paper also documents reduced systemic risk around positive jumps and regime shifts of allowances and increased systemic risk throughout negative jumps and regime shifts. Our contribution to the literature consists in producing evidence that financial stability supports the European carbon market initiative to mitigate climate change. These results have significant implications for policymakers and financial institutions.

Credit Card Fraud Detection By IJISRt

While credit card fraud and abuse are the way becoming more common, the convenience using of there credit for the way online purchases has also improved. These fraudulent activities pose a severe financial danger to both credit the are card users and way they using it financial institutions. The first thinking aims to do of this research project is to recognize and put an end to these kinds of fraudulent activities. It addresses a broad range of subjects, including the frequency of false positives, imbalanced datasets, evolving fraud trends, and restricted public data access. The literature now under publication offers a range of machine learning-based methods, including logistic regression, decision trees, random the forests, support to the vector of the +machines, and XG Boost, with the purpose of identifying credit card fraud. However, these methods often exhibit lower accuracy rates, highlighting the need for more advanced deep learning algorithms in order to effectively lower fraud losses. Therefore, the of way they primary thinks objective of this is to improve fraud detection abilities through which the way this application most be of state- of-the-art deep learning algorithms. An assessment way it should be of the research project reveals better outcomes, including optimized AUC curves, precision, f1-score, and accuracy. The ultimate objective there are is to create models that will greatly improve credit card fraud detection and prevention. This research focuses on advanced deep learning techniques in to the way it is order they provide the to more reliable and accurate fraud detection mechanisms, enhance security for credit card users, and lower financial risks for financial institutions when conducting online transactions.

Leveraging Machine Learning Algorithm to Enable Access to Credit for Small Businesses in the United States of America

The research centers on the essential function of loans in driving economic expansion and the intrinsic danger of loan defaults. Small businesses play a crucial role in generating employment and fostering economic growth, highlighting the significance of precise loan eligibility evaluations. The Small Business Administration's (SBA) 7(a) loan program seeks to assist small businesses by providing loan guarantees to mitigate risks for financial institutions (Mini et al., 2018). The study utilizes sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to forecast loan defaults and ascertain ideal loan amounts for small businesses. A dataset comprising 27 variables is analyzed, encompassing loan attributes, borrower details, and loan outcomes. The results highlight the effectiveness of both Random Forest and XGBoost in generating loan default predictions, with a slight edge for XGBoost. Additionally, Linear Regression is used to estimate loan amounts for qualified borrowers. The analysis identifies key factors contributing to loan defaults, with variables such as Term, Disbursement Gross, SBA Approval, and Gross Approval playing a significant role in Random Forest predictions. The study reveals intriguing patterns in loan defaults across various industrial sectors, emphasizing the complex nature of assessing loan performance within these industries (Zhou et al., 2023). This research aims to improve lending practices, benefiting both lenders and borrowers while enhancing our understanding of risk management in the context of small businesses.

Retracted: Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions

Retracted: Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions

Research on Enterprise Regulation under the Background of Financial Technology

With the development and maturity of technology, the financial industry has become increasingly connected to technology. In recent years, fintech companies have flourished in the financial industry and are gradually becoming an important mainstay of global business empowerment. Fintech has opened up new areas of financial business and driven the development of finance and society. It has revolutionized the financial industry with the high efficiency brought by technology, but at the same time, it has also produced adverse events such as Alipay's annual bill exposing personal privacy and P2P mines, which have brought new risks for financial institutions and investors in general, and therefore it is urgent to take strict measures to regulate it. This paper starts from the connotation of fintech, briefly describes the development history of fintech in China, and puts forward corresponding countermeasures and suggestions on how to better regulate fintech enterprises based on the high-frequency risks faced by fintech enterprises and the internal operational deficiencies of the regulatory authorities.

Enterprise Management and Risk Assessment Analysis Practice

Finance is the core competitiveness of the country. Since the reform and opening up, China's financial industry has developed rapidly and made historic achievements, but there are still many contradictions and problems that make it difficult to meet the requirements of high-quality economic development. First, the debt problem: China's enterprises, local governments and household sectors are all facing considerable debt pressure. Some enterprises and local governments have hidden debts, and the debt of the household sector is also increasing. This poses a risk to the stability of the financial system.Second, financial institution risk: Some financial institutions have risks, including credit risk and liquidity risk. Some non-bank financial institutions have relatively low capital adequacy ratios and are potentially at risk of default.Third, financial chaos: some financial chaos problems still exist, such as illegal fund-raising, online financial fraud and so on. These issues challenge the effectiveness of financial regulation and public trust in the financial system.Fourth, structural problems in the financial system: there are some structural problems in China's financial system, such as insufficient interaction between the banking sector and the capital market, and relatively low efficiency of financial resource allocation. These problems constrain the development of the financial system and sustainable economic growth.

Роль бухгалтерського обліку в управлінні ризиками на прикладі фінансових інститутів

The article discusses the role of accounting in identifying and managing risks, in particular on the example of financial institutions. The specifics of financial institutions and their risks, in particular credit, interest, liquidity and market risks, are studied. The article defines the role of accounting in assessing these risks, as well as in managing them through internal control, analysis of financial statements and development of risk management strategies. Specific examples and guidance are provided on how accountants can identify, assess, and minimize risk in financial institutions while ensuring compliance with norms and regulations. The aim of the study is to substantiate the need to involve specialists in the accounting and analytical profile in the process of making effective decisions on risk management of a financial entity. In modern conditions, financial institutions are in an unstable economic, political and technological environment, which leads to a significant increase in uncertainty. This creates a complex set of potential risks that, if mismanaged, can lead to losses, damage, or even destroy the company’s business model and reputation. One of the key competencies of accountants today is the ability to identify potential risks in time to avoid them or effectively manage them. The scientific novelty of the study lies in the disclosure of the expediency of involving accountants in the decision-making process and the development of a policy of integrated risk management of a financial entity in order to preserve its value. It is concluded that a professional accountant is important in risk management for financial institutions and the role of a proactive approach to risk management, in which accountants are key players, is emphasized. It is emphasized that integrated risk management should be considered as an important aspect of modern business, which helps organizations to effectively manage various types of risks, including financial, operational, reputational, etc. This approach allows companies to more accurately analyze, assess, and manage risks, minimizing potential losses and increasing the likelihood of achieving their goals. Integrated risk management involves the implementation of specific strategies, processes, instruments, and systems that are integrated into the company’s overall strategy and business processes. This approach also contributes to increased transparency and improved corporate governance, which in turn increases trust from investors, partners, and customers. In addition, integrated risk management enables companies to be more adaptable to changing market conditions and respond quickly to potential threats and opportunities.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

How real estate bubbles affect the systemic risk of financial institutions in the United Arab Emirates

The paper addresses a crucial gap in the literature by examining the interplay between real estate price bubbles and systemic risk in the United Arab Emirates (UAE) from 2006 to 2022. The paper employs a three-step testing procedure: bubble detection using the bootstrapped GSADF test, measuring systemic risk using Delta-CoVaR and MES measures, and assessing the impact of real estate bubbles on bank risk through panel data regression. Utilizing a sample of 17 conventional banks operating in the UAE, the study demonstrates that the interplay between real estate price bubbles and systemic risk is influenced by the specific characteristics of banks. Higher levels of loan growth, leverage, and bank size heighten the systemic risk faced by banks during asset price bubbles. Interestingly, the results also indicate that banks with a greater degree of income diversification contribute less to systemic risk during periods characterized by real estate bubbles. The results from this study are useful for policymakers in designing and implementing regulations to stabilize and prevent the UAE's banking sector from being affected by real estate price bubbles.

On the wicked problem of quantifying and managing non-financial risks and the role of digital technology in providing solutions

The management of operational risk in financial institutions has all the characteristics of a ‘wicked problem’. Certainly, the repeated efforts of the Bank of International Settlements, (BIS) Basel Committee on Banking Supervision (BCBS) to have banks control and mitigate their operational risks speak to the tractability of extant approaches to addressing them effectively. The original ‘Principles for the Sound Management of Operational Risk’1 and its recent revisions,2 the BCBS ‘Principles for Effective Risk Data Aggregation and Risk Reporting’3 and the ‘Principles for Operational Resilience’,4 collectively offer a sound foundation for addressing this enduring problem. Why then are solutions so elusive for banks to implement? This paper first outlines the institutional and social web of conditions and factors that contribute to the existence of this ‘wicked problem’. It then identifies how AI-based digital technologies can once and for all effectively address the problem of operational risk in large banks. Nevertheless, as powerful as today's digital technologies are, they require an organising vision, particularly if they are to contribute to the management of operational risk. This paper informs such a vision and identifies a comprehensive artificial intelligence-based digital architecture to realise it.

Climate Change Shocks and Credit Risk of Financial Institutions: Evidence from China’s Commercial Banks

ABSTRACT As global climate risk is looming in recent years, the climate risks faced by financial institutions are also increasing. Effectively quantifying and assessing climate-related financial risks is of great significance for financial institutions to establish risk management mechanisms. Accordingly, choosing Chinese commercial bank data from 2007 to 2019, this paper quantitatively evaluates the influence of climate change shock on the banks’ credit risk by taking the annual temperature fluctuation in cities as the core index to characterize the climate change degree. It is found that annual average temperature rise can significantly increase the credit risk level of commercial banks. Heterogeneity analysis indicates that small-scale banks, rural commercial banks, and commercial banks with higher marketization levels are more sensitive to climate change. Further analysis shows summer and autumn temperature changes have the most prominent impact on banks’ credit risk. Moreover, there is no obvious nonlinear relationship or lagged effects between annual temperature change and banks’ credit risk in our dataset.

Revisiting corporate governance and financial risk‐taking

AbstractCorporate governance attributes have varying effects on risk taking when variables are examined separately. We study the effects of a large range of corporate governance attributes on risk taking using a comprehensive US sample. Our findings confirm that although there are certain characteristics that drive this positive effect such as compensation structure, there are those which have the opposite effect such as board‐level attributes. Our paper contributes to the broader literature on the relationship between corporate governance and risk in financial institutions, which are often overlooked in traditional studies. We shed light on the importance of studying corporate governance at a granular level rather than using a single index. The findings offer insights to regulators in determining suitable corporate governance frameworks to ensure the protection of investors rights in financial institutions.

Does Public Corruption Affect Bank Failures? Evidence from the United States

Corruption influences firm behavior and performance even in relatively transparent countries like the United States. In this paper, we examine whether corruption at the state level affected bank failures during the subprime mortgage crisis. Our measure of corruption is the number of corruption convictions of government employees (adjusted for population) based on the Public Integrity Section (PIN) reports from the Department of Justice, capturing the degree of “public corruption” in the US. After disaggregating the data based on bank size and geography, we find that corruption is associated with more bank failures for smaller banks and fewer bank failures for banks located in the South. This research marks a pioneering attempt to examine the connection between corruption and bank failures while underscoring the significance of political risk for financial institutions. Given the recent setbacks experienced by Silicon Valley Bank, Signature Bank, and First Republic Bank, this research provides valuable recommendations for policymakers. The findings suggest the need for regulators to mandate greater transparency regarding banks’ exposure to undisclosed risks, such as political risk. It also advocates for implementing internal control mechanisms to curb corrupt activities.

Interpretable high-stakes decision support system for credit default forecasting

Methods for forecasting credit default have long been research focus for financial institutions. In this study, we propose an interpretable high-stakes decision support system for credit default forecasting called CDFS. Because of the high-stake nature of credit default prediction, the proposed CDFS adheres to the principle of "people in the loop." The proposed CDFS comprises six modules: data processing, feature selection, data balancing, forecasting, evaluation, and interpretation. A feature selection method (permutation importance method), nine resampling methods, and six high-performance forecasting methods were employed in the proposed CDFS. The China Taiwan credit card default dataset and South Germany credit dataset were used to test the interpretability and predictive performance of the proposed CDFS. Experiments showed that the feature selection and data balancing modules of the CDFS effectively improve the prediction performance. A comparison with traditional logistic regression models demonstrated that the CDFS can provide decision-makers with satisfactory explanations for prediction results. In summary, the CDFS proposed in this study exhibited excellent predictive performance and satisfactory interpretability. This study contributes to improving the accuracy of credit default forecasting and reducing credit risk in financial institutions.

Effective risk management for financial institutions’ partnerships with FinTechs

The aim of this paper is to provide insights into the regulatory concerns and challenges faced by financial institutions when partnering with FinTech companies. The paper highlights the recent consent orders issued against well-known financial institutions for their FinTech relationships and risk management programme shortcomings, as well as how financial institution and FinTech collaboration can overcome these shortcomings. It acknowledges the frustration caused by the limited guidance provided by regulators on incorporating FinTech into financial institution operations. However, it encourages financial institutions to learn from the published consent orders and improve their risk management programmes. The paper also discusses the factors that need to be considered in FinTech partnerships, such as overborrowing, bias and discrimination in algorithms, and data privacy and reliability. It emphasises the importance of compliance professionals in analysing regulatory considerations early and creatively to meet future demands. Furthermore, the paper explores the changing landscape and increasing reliance on technology, highlighting areas that need to be considered in financial institution risk management programmes. It addresses concerns related to alternative credit models, bias and discrimination in algorithms, and data privacy and reliability. Overall, readers can expect to gain knowledge and insights into the regulatory concerns and challenges associated with FinTech partnerships, as well as practical actions and considerations to mitigate risks and ensure compliance.

An innovative model fusion algorithm to improve the recall rate of peer-to-peer lending default customers

Peer-to-peer (P2P) lending is a fintech innovation that provides loans to individuals and businesses without the need for additional intermediaries. However, if the borrower fails to repay the loan on time, the bank suffers a financial loss due to the borrower's default. At present, many studies are trying to improve the accuracy of credit default risk prediction models to reduce the risk of financial institutions' loan business, but it is also a meaningful study to focus on improving the recall rate of model prediction results. In the related research field of credit default risk prediction, improving the recall rate is crucial for banks and other lending institutions. The recall rate refers to the proportion of all true positive examples that are correctly identified as positive examples. In credit default risk prediction, true positive cases refer to the cases where borrowers default, while being correctly identified as positive cases means that the model can accurately predict which borrowers may default. If banks' risk prediction models can improve recall rates, this can help them better assess risk, formulate appropriate lending policies, and minimize default losses. This study aims to further improve the recall and AUC metrics (area under the ROC curve) of P2P credit default prediction using the lending dataset from Lending Club using an improved machine learning model fusion algorithm. Our proposed algorithm consists of two machine learning algorithms. The improved LightGBM algorithm and the improved XGBoost algorithm are used for model fusion to obtain the LGB-XGB-Stacking model. By optimizing the evaluation metrics in the training phase of these two algorithms, we have achieved significant improvement in results, especially in the recall rate of defaulted customers and the overall AUC metrics. After comparing the predictive performance of the models, our proposed predictive model is improved in the following aspects. First, the recall of our proposed prediction model is significantly better than other models. Second, it also outperforms other machine learning models on the AUC metric. Among them, the recall rate of the positive sample (default customer) is 24.43% higher than that of the XGBoost model, and the overall AUC index is 6.71% higher. In the end, it was found that XGBoost, LightGBM, and CatBoost models performed very well in terms of accuracy rate improvement. The accuracy rates of these three models are very close, and they are all higher than other models. Therefore, it is found that machine learning models are still an effective method for credit default prediction research, especially tree models. Although the accuracy of our model is slightly lower than the above models, our proposed model outperforms the above models in identifying defaulting customers. Our model can more accurately identify defaulting customers and minimize the risk of bad debts for financial institutions.