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Keywords = bank default risk

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16 pages, 757 KiB  
Article
Do Fintech Lenders Align Pricing with Risk? Evidence from a Model-Based Assessment of Conforming Mortgages
by Zilong Liu and Hongyan Liang
FinTech 2025, 4(2), 23; https://doi.org/10.3390/fintech4020023 - 9 Jun 2025
Viewed by 769
Abstract
This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), [...] Read more.
This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), followed closely by banks (0.857), with fintech lenders trailing (0.852). In pricing analysis, banks adjust the origination rates most sharply with borrower risk (7.20 basis points per percentage-point increase in default probability) compared to fintech (4.18 bp) and non-fintech lenders (5.43 bp). Fintechs underprice 32% of high-risk loans, highlighting limited incentive alignment under GSE securitization structures. Expanding the allowable alternative data and modest risk-retention policies could enhance fintechs’ analytical effectiveness in mortgage markets. Full article
(This article belongs to the Special Issue Trends and New Developments in FinTech)
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18 pages, 283 KiB  
Article
Inferred Loss Rate as a Credit Risk Measure in the Bulgarian Banking System
by Vilislav Boutchaktchiev
Mathematics 2025, 13(9), 1462; https://doi.org/10.3390/math13091462 - 29 Apr 2025
Viewed by 341
Abstract
The loss rate of a bank’s portfolio traditionally measures what portion of the exposure is lost in the case of a default. To overcome the difficulties involved in its computation due to, e.g., the lack of private data, one can utilize an inferred [...] Read more.
The loss rate of a bank’s portfolio traditionally measures what portion of the exposure is lost in the case of a default. To overcome the difficulties involved in its computation due to, e.g., the lack of private data, one can utilize an inferred loss rate (ILR). In the existing literature, it has been demonstrated that this indicator has sufficiently close properties to the actual loss rate to facilitate capital adequacy analysis. The current study provides complete mathematical proof of an earlier-stated conjecture, that ILR can be instrumental in identifying a conservative upper bound of the capital adequacy requirement of a bank credit portfolio, using the law of large numbers and other techniques from measure-theory-based probability. The assumptions required in this proof are less restrictive, reflecting a more realistic view. In the current study, additional empirical evidence of the usefulness of the indicator is provided, using publicly available data from the Bulgarian National Bank. Despite the definite conservativeness of the capital buffer implied from the analysis of ILR, the empirical analysis suggests that it is still within the regulatory limits. Analyzing ILR together with the Inferred Rate of Default, we conclude that the indicator provides signals about a bank portfolio’s credit risk that are relevant, timely, and adequately inexpensive. Full article
(This article belongs to the Section E: Applied Mathematics)
27 pages, 1863 KiB  
Article
The Impact of Bank Fintech on Corporate Short-Term Debt for Long-Term Use—Based on the Perspective of Financial Risk
by Weiyu Wu and Xiaoyan Lin
Int. J. Financial Stud. 2025, 13(2), 68; https://doi.org/10.3390/ijfs13020068 - 16 Apr 2025
Cited by 1 | Viewed by 1205
Abstract
Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates [...] Read more.
Information asymmetry between banks and enterprises in the credit market is essentially the microfoundation of financial risk generation. The frequent occurrence of corporate debt defaults, mainly due to the behavior of short-term debt for long-term use (hereinafter referred to as “SDLU”), further aggravates the contagion path from individual liquidity crisis to systemic repayment crisis. In order to test whether bank financial technology (hereinafter referred to as “BankFintech”) can mitigate SDLU and reduce the possibility of financial risks, this study matched the loan data of China’s A-share listed companies with the patent data of bank-invented Fintech from 2013 to 2022 to construct the BankFintech Development Index for empirical analysis. The empirical results show that the development of BankFintech can significantly inhibit SDLU. The mechanism test reveals that BankFintech reduces bank credit risk and liquidity risk by lowering firms’ risk-weighted assets, improving capital adequacy and liquidity ratios, tilts banks’ lending preferences toward duration-matched long-term financing, and “forces” enterprises to take the initiative to improve their financial health and information transparency, enhance their ability to obtain long-term loans, and realize the active management of mismatch risk. Heterogeneity analysis finds that the effect is more significant in non-state-owned enterprises and technology-intensive industries. Further analysis shows that the level of enterprise digitization, the intensity of financial regulation, and related financial policies significantly moderate the marginal effect between the two. This study verified the “Porter’s Risk Mitigation Hypothesis” of Fintech, providing empirical evidence for effectively cracking the financial vulnerability caused by debt maturity mismatch and deepening financial supply-side reform. Full article
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25 pages, 4215 KiB  
Article
A Real Option Approach to the Valuation of the Default Risk of Residential Mortgages
by Angela C. De Luna López, Prosper Lamothe-López, Walter L. De Luna Butz and Prosper Lamothe-Fernández
Int. J. Financial Stud. 2025, 13(1), 31; https://doi.org/10.3390/ijfs13010031 - 1 Mar 2025
Viewed by 984
Abstract
A significant share of many commercial banks’ portfolios consists of residential mortgage loans provided to individuals and families. This paper examines the default and rational prepayment risk of single-borrower (residential) mortgage loans based on an option pricing model that captures the skewness and [...] Read more.
A significant share of many commercial banks’ portfolios consists of residential mortgage loans provided to individuals and families. This paper examines the default and rational prepayment risk of single-borrower (residential) mortgage loans based on an option pricing model that captures the skewness and kurtosis of the house prices returns’ distribution via the shifted lognormal distribution. Equilibrium option-adjusted credit spreads are obtained from the implementation of the model under plausible values of the relevant parameters. The methodology involves numerical experiments, using a shifted binomial tree model by Haathela and Camara and Chung, to evaluate the effects of the loan-to-value (LTV) ratio, asset volatility, interest rates, and recovery costs on mortgage valuation. Findings indicate prepayment risk significantly influences loan value, as it limits upside potential, while LTV and volatility directly impact default risk. The shifting parameter (θ) in the asset distribution proves essential for accurate risk assessment. Conclusions emphasize the need for mortgage underwriting to consider specific asset characteristics, optimal loan structures, and prevailing risk-free rates to avoid underestimating risk. This model can aid in the more robust pricing and management of mortgage portfolios, especially relevant in regions with substantial mortgage-backed exposure, such as the European banking system. Full article
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30 pages, 3246 KiB  
Article
Can We Use Financial Data to Predict Bank Failure in 2009?
by Shirley (Min) Liu
J. Risk Financial Manag. 2024, 17(11), 522; https://doi.org/10.3390/jrfm17110522 - 19 Nov 2024
Cited by 1 | Viewed by 912
Abstract
This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency (OIOE) to forecast [...] Read more.
This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency (OIOE) to forecast bank failure. Using the bank failure list from the Federal Deposit Insurance Corporation (FDIC) database, I match the banks that failed in 2009 with a control sample based on geography, size, the ratio of total loans to total assets, and the age of banks. The model suggested by this paper could predict correctly up to 94.44% (97.15%) for the failure (and non-failure) of banks, with an overall 96.43% prediction accuracy, (p = 0.5). Specifically, the stepwise logistic regression suggests some proxies for capital adequacy, assets/loan risk, profit efficiency, earnings, and liquidity risk to be the predictors of bank failure. These results partially agree with previous studies regarding the importance of certain variables, while offering new findings that the three proposed proxies for LQ, IntMag, and OIOE statistically and economically significantly impact the probability of bank failure. Full article
(This article belongs to the Section Business and Entrepreneurship)
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33 pages, 9119 KiB  
Article
Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers
by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra and Jiabin Luo
Risks 2024, 12(11), 174; https://doi.org/10.3390/risks12110174 - 4 Nov 2024
Cited by 13 | Viewed by 24926
Abstract
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify [...] Read more.
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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19 pages, 1817 KiB  
Article
Modeling Risk Sharing and Impact on Systemic Risk
by Walter Farkas and Patrick Lucescu
Mathematics 2024, 12(13), 2083; https://doi.org/10.3390/math12132083 - 2 Jul 2024
Cited by 2 | Viewed by 1572
Abstract
This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on credit default swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that [...] Read more.
This paper develops a simplified agent-based model to investigate the dynamics of risk transfer and its implications for systemic risk within financial networks, focusing specifically on credit default swaps (CDSs) as instruments of risk allocation among banks and firms. Unlike broader models that incorporate multiple types of economic agents, our approach explicitly targets the interactions between banks and firms across three markets: credit, interbank loans, and CDSs. This model diverges from the frameworks established by prior researchers by simplifying the agent structure, which allows for more focused calibration to empirical data—specifically, a sample of Swiss banks—and enhances interpretability for regulatory use. Our analysis centers around two control variables, CDSc and CDSn, which control the likelihood of institutions participating in covered and naked CDS transactions, respectively. This approach allows us to explore the network’s behavior under varying levels of interconnectedness and differing magnitudes of deposit shocks. Our results indicate that the network can withstand minor shocks, but higher levels of CDS engagement significantly increase variance and kurtosis in equity returns, signaling heightened instability. This effect is amplified during severe shocks, suggesting that CDSs, instead of mitigating risk, propagate systemic risk, particularly in highly interconnected networks. These findings underscore the need for regulatory oversight to manage risk concentration and ensure financial stability. Full article
(This article belongs to the Special Issue Mathematical Developments in Modeling Current Financial Phenomena)
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19 pages, 3456 KiB  
Review
The Von Neumann–Morgenstern Curve and Bank Capital Adequacy Penalties—An Empirical Analysis
by Thomas Draper and Stefano Cavagnetto
Economies 2024, 12(6), 150; https://doi.org/10.3390/economies12060150 - 13 Jun 2024
Viewed by 1066
Abstract
The risk of lending money collected from savers is that it leaves banks liable to default with depositors if events (and hence repayment demands) become ‘abnormal’. Even though international and national regulation has been introduced to ensure that a certain level of capital [...] Read more.
The risk of lending money collected from savers is that it leaves banks liable to default with depositors if events (and hence repayment demands) become ‘abnormal’. Even though international and national regulation has been introduced to ensure that a certain level of capital is retained by banks, such regulation can be subverted. The current system of international regulation based on the Basel III agreements does not stipulate a standardised approach for inspection frequency or penalty magnitude. This leaves the potential for regulatory arbitrage. The scientific value of an analysis to optimise regulatory efficiency and reduce such arbitrage is therefore considerable. This work therefore assesses the results of the empirical testing of a model based on the Von Neumann–Morgenstern utility function and consequently proposes that this model be used as a basis for standardising capital adequacy limit infraction penalties on an international level to prevent regulatory arbitrage. A survey is undertaken in order to test the responses of participants on the level of penalty which would deter them from regulatory transgression under different theorised levels of profit and probability of discovery. Based on the responses of two distinct subject groups (‘bankers’ and ‘non-bankers’) in different scenarios of hypothetical capital adequacy violation, the Von Neumann–Morgenstern utility function is reviewed against empirical results and revealed to show a semi-strong correlation. Lastly, the analysis reveals the striking similarities of the two groups’ responses, posing regulatory implications for the industry. Full article
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17 pages, 3128 KiB  
Article
An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection
by Shaona Hua, Chunying Zhang, Guanghui Yang, Jinghong Fu, Zhiwei Yang, Liya Wang and Jing Ren
Mathematics 2024, 12(11), 1695; https://doi.org/10.3390/math12111695 - 29 May 2024
Cited by 2 | Viewed by 1456
Abstract
Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To [...] Read more.
Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities. Full article
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26 pages, 3179 KiB  
Article
Systemic Risk Arising from Shadow Banking and Sustainable Development: A Study of Wealth Management Products in China
by Hongjie Pan and Hong Fan
Sustainability 2024, 16(10), 4280; https://doi.org/10.3390/su16104280 - 19 May 2024
Cited by 1 | Viewed by 2503
Abstract
Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. [...] Read more.
Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. We analyze the data obtained from a rich sample of 31 listed commercial banks in China and shadow banking represented by wealth management products (WMPs) by constructing a dynamic complex interbank network model. The results show that the risks and vulnerabilities generated by shadow banking spread out through the interbank network and cause systemic risk to increase. The effect operates through increasing the number of default banks, reducing banks’ survival rate and profit, and forcing central bank bailout funds expansion. However, it has a positive impact in terms of augmenting liquidity and enhancing investment opportunities. Furthermore, the variability in the influence of different categories of shadow banking is assessed, emphasizing that short-term shadow banking exerts a more pronounced impact on systemic risk. In addition, the heterogeneity of the shadow banking effect on different types of commercial banks is explored, revealing that local and rural commercial banks experience a more conspicuous effect compared to state-owned and joint-stock banks. Our findings highlight that improving external supervision, promoting financial internal governance, and constraining credit linkages are vital for alleviating the increase in risks in shadow banking and maintaining the sustainable development of banking. Full article
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15 pages, 289 KiB  
Article
Optimizing Ensemble Learning to Reduce Misclassification Costs in Credit Risk Scorecards
by John Martin, Sona Taheri and Mali Abdollahian
Mathematics 2024, 12(6), 855; https://doi.org/10.3390/math12060855 - 14 Mar 2024
Cited by 2 | Viewed by 1850
Abstract
Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we compared the risk estimation of 26 widely [...] Read more.
Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we compared the risk estimation of 26 widely used machine learning algorithms based on commonly used statistical metrics. The best-performing algorithms were then used for model selection in ensemble learning. For the first time, we proposed financial criteria that assess the impact of losses associated with both false positive and false negative predictions to identify optimal ensemble learning. The German Credit Dataset (GCD) is augmented with simulated financial information according to a hypothetical mortgage portfolio observed in UK, European and Australian banks to enable the assessment of losses arising from misclassification costs. The experimental results using the simulated GCD show that the best predictive individual algorithm with the accuracy of 0.87, Gini of 0.88 and Area Under the Receiver Operating Curve of 0.94 was the Generalized Additive Model (GAM). The ensemble learning method with the lowest misclassification cost was the combination of Random Forest (RF) and K-Nearest Neighbors (KNN), totaling USD 417 million in costs (USD 230 for default costs and USD 187 for opportunity costs) compared to the costs of the GAM (USD 487, USD 287 and USD 200). Implementing the proposed financial criteria has led to a significant USD 70 million reduction in misclassification costs derived from a small sample. Thus, the lending institutions’ profit would considerably rise as the number of submitted credit applications for approval increases. Full article
(This article belongs to the Section E: Applied Mathematics)
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22 pages, 563 KiB  
Article
ECB Monetary Policy and the Term Structure of Bank Default Risk
by Tom Beernaert, Nicolas Soenen and Rudi Vander Vennet
J. Risk Financial Manag. 2023, 16(12), 507; https://doi.org/10.3390/jrfm16120507 - 7 Dec 2023
Viewed by 2644
Abstract
Euro Area banks have been confronted with unprecedented monetary policy actions by the ECB. Monetary policy may affect bank risk profiles, but the consequences may differ for short-term risk versus long-term or structural bank risk. We empirically investigated the association between the ECB’s [...] Read more.
Euro Area banks have been confronted with unprecedented monetary policy actions by the ECB. Monetary policy may affect bank risk profiles, but the consequences may differ for short-term risk versus long-term or structural bank risk. We empirically investigated the association between the ECB’s monetary policy stance and market-perceived short-term and long-term bank risk, using the term structure of default risk captured by bank CDS spreads. The results demonstrated that, during the period 2009–2020, ECB expansionary monetary policy diminished bank default risk in the short term. However, we did not observe a similar decline in long-term bank default risk, since we documented that monetary stimulus is associated with a steepening of the bank default risk curve. The reduction of bank default risk was most pronounced during the sovereign debt crisis and for periphery Euro Area banks. From 2018 onwards, monetary policy accommodation is associated with increased bank default risk, both short-term and structurally, which is consistent with the risk-taking hypothesis under which banks engage in excessive risk-taking behavior in their loan and securities portfolios to compensate profitability pressure caused by persistently low rates. The increase in perceived default risk is especially visible for banks with a high reliance on deposit funding. Full article
(This article belongs to the Section Banking and Finance)
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14 pages, 319 KiB  
Article
Inferred Rate of Default as a Credit Risk Indicator in the Bulgarian Bank System
by Vilislav Boutchaktchiev
Entropy 2023, 25(12), 1608; https://doi.org/10.3390/e25121608 - 30 Nov 2023
Cited by 4 | Viewed by 1365
Abstract
The inferred rate of default (IRD) was first introduced as an indicator of default risk computable from information publicly reported by the Bulgarian National Bank. We have provided a more detailed justification for the suggested methodology for forecasting the IRD on the bank-group- [...] Read more.
The inferred rate of default (IRD) was first introduced as an indicator of default risk computable from information publicly reported by the Bulgarian National Bank. We have provided a more detailed justification for the suggested methodology for forecasting the IRD on the bank-group- and bank-system-level based on macroeconomic factors. Furthermore, we supply additional empirical evidence in the time-series analysis. Additionally, we demonstrate that IRD provides a new perspective for comparing credit risk across bank groups. The estimation methods and model assumptions agree with current Bulgarian regulations and the IFRS 9 accounting standard. The suggested models could be used by practitioners in monthly forecasting the point-in-time probability of default in the context of accounting reporting and in monitoring and managing credit risk. Full article
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33 pages, 3535 KiB  
Article
Blockchain-Enabled Supply Chain Internal and External Finance Model
by Quanpeng Chen and Xiaogang Chen
Sustainability 2023, 15(15), 11745; https://doi.org/10.3390/su151511745 - 30 Jul 2023
Cited by 6 | Viewed by 3089
Abstract
This study applies Stackelberg game theory to analyze and compare optimal operational strategies in four supply chain finance scenarios: traditional trade financing (TI), trade financing through the blockchain platform (BI), traditional external financing (TE), and external financing through the blockchain platform (BE). The [...] Read more.
This study applies Stackelberg game theory to analyze and compare optimal operational strategies in four supply chain finance scenarios: traditional trade financing (TI), trade financing through the blockchain platform (BI), traditional external financing (TE), and external financing through the blockchain platform (BE). The main findings are as follows: First, the adoption of the blockchain platform reduces the interest rate threshold, making external financing more advantageous for retailers with higher capital constraint. Further, financing through the blockchain platform leads to higher wholesale prices, retail prices, and order quantities compared to traditional financing scenarios. Second, internal trade financing and the use of blockchain technology are preferred over external bank financing. However, conducting external bank financing through the blockchain platform yields greater profit growth for manufacturers and retailers. Accessing the blockchain platform is the optimal strategy for retailers and banks, leading to a favorable “multi-win” situation when the manufacturer’s platform fees are reasonable. Third, the manufacturer’s risk guarantee ratio plays a crucial role in determining the choice of financing mode, particularly when the retailer faces the risk of debt default. This study contributes to the literature by quantifying the impacts of blockchain technology deployment for three aspects that have been overlooked in previous studies: the set-up cost and access fee of the blockchain platform, the service level provided by the platform, and the demand increase resulting from blockchain technology adoption. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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15 pages, 2979 KiB  
Systematic Review
Quantifying Causality between Climate Change and Credit Risk: A Bibliometric Study and Research Agenda
by Noluthando Mngadi and Hossana Twinomurinzi
Sustainability 2023, 15(12), 9319; https://doi.org/10.3390/su15129319 - 9 Jun 2023
Cited by 1 | Viewed by 2754
Abstract
There is increasing pressure on organisations and countries to manage the financial risks associated with climate change. This paper summarises research on climate change, credit risk and the associated losses, and specifically identifies methods that could contribute to quantifying the causal relationships between [...] Read more.
There is increasing pressure on organisations and countries to manage the financial risks associated with climate change. This paper summarises research on climate change, credit risk and the associated losses, and specifically identifies methods that could contribute to quantifying the causal relationships between climate change and credit risk. We conducted a bibliometric analysis using the Web of Science database to analyse 3138 documents that investigated climate change and credit risk. The key results reveal that climate change has a quantifiable effect on credit risk, and that the most affected industries or sectors are energy, transportation/mobility, agriculture and food, manufacturing, and construction. The prominent methods to quantify causal relationships between climate change and credit risk are regression models, but these are mostly used in preliminary and testing stages. Distance to default and credit risk are the main areas of focus when quantifying climate change and credit risk. Banks are the main type of organisation that have sought to quantify the causal relationship. We identify a research agenda to quantify these causal relationships. Full article
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