Risk Of Financial Institutions Research Articles

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.

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.

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.

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.

Joint loan risk prediction based on deep learning‐optimized stacking model

AbstractIn recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

Early warning model and prevention of regional financial risk integrated into legal system.

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.

Calculating Strategic Risk in Financial Institutions.

Banks face many intangible hazards that are difficult to calculate. Strategic risk is one of the most critical factors affecting a bank's profitability, financial strength, and commercial success. The impact of risk on profit may be insignificant in the short term. Still, it may become highly significant in the medium and long term, with the potential to cause substantial financial losses and impair bank stability. Hence, strategic risk management is an important endeavor that must be carried out according to the rules set out under the Basel II framework. Analysis of strategic risk is a relatively new research enterprise. The current literature addresses the need to manage this risk and links it to the concept of economic capital, the amount of capital that a company should hold to survive such a risk. However, an action plan has yet to be produced. This paper attempts to address this gap by providing a mathematical analysis of the probability and effect of different strategic risk factors. Specifically, we develop a methodology for calculating a metric of strategic risk in terms of a bank's risk assets. Furthermore, we suggest a way of integrating this metric into the calculation of the capital adequacy ratio.

Share pledge financing network and systemic risks: Evidence from China

We document a unique determinant of financial systemic risks in China, share pledge financing (SPF) network, by studying all listed Chinese financial institutions that provide SPF business. As one of the most popular refinancing tools in China, SPF formulates a network among financial institutions with common collaterals. We propose a centrality measure to quantify such network effects, and document that banks are more important than securities in the SPF network before 2018, but this pattern has reversed afterwards. SPF network effects, rather than SPF margin closeout risks, significantly increase systemic risks of China's financial institutions, by increasing both individual risks of financial institutions and connectedness among them. Such an impact of the SPF network on systemic risks is more pronounced in OTC markets than in exchanges. The SPF network affects systemic risks in more extreme cases, while SPF margin closeout risks affect systemic risks in more moderate cases.

Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk.

How do empirical estimators of popular risk measures impact pro-cyclicality?

AbstractRisk measurements are clearly central to risk management, in particular for banks, (re)insurance companies, and investment funds. The question of the appropriateness of risk measures for evaluating the risk of financial institutions has been heavily debated, especially after the financial crisis of 2008/2009. Another concern for financial institutions is the pro-cyclicality of risk measurements. In this paper, we extend existing work on the pro-cyclicality of the Value-at-Risk to its main competitors, Expected Shortfall, and Expectile: We compare the pro-cyclicality of historical quantile-based risk estimation, taking into account the market state. To characterise the latter, we propose various estimators of the realised volatility. Considering the family of augmented GARCH(p, q) processes (containing well-known GARCH models and iid models, as special cases), we prove that the strength of pro-cyclicality depends on the three factors: the choice of risk measure and its estimators, the realised volatility estimator and the model considered, but, no matter the choices, the pro-cyclicality is always present. We complement this theoretical analysis by performing simulation studies in the iid case and developing a case study on real data.

Multi-Party Evolutionary Game Analysis of Accounts Receivable Financing under the Application of Central Bank Digital Currency

Accounts receivable financing is one of the most prominent financing approaches in supply chain finance; nevertheless, in the actual financing process, financial institutions and SMEs have credit risk and information asymmetry risk, which leads to frequent nonpayment and collaboration fraudulent loans. This paper introduces central bank digital currency into traditional accounts receivable financing and solves the credit risk and information asymmetry risk using two technologies of central bank digital currency: digital technology and blockchain technology; digital technology enables the supervision of capital flow, and blockchain technology enables for access to logistics and information flow. In the context of using central bank digital currency technology, this paper builds an evolutionary game model of whether financial institutions use central bank digital currency and whether SMEs repay the loan, compares the evolutionary stabilization strategies of financial institutions and SMEs, calculates and analyzes the model’s impact, investigates changes in the decision-making and evolutionary paths of both parties, and then conducts numerical simulation analysis using Matlab and Python to verify the model’s reliability further. According to the results, adding central bank digital currency to the traditional accounts receivable financing model can reduce the loan risk of financial institutions, increase the credibility of accounts receivable financing, expedite the implementation of accounts receivable financing, and alleviate the financing concerns of SMEs.