Risks

18 pages, 664 KB

Open AccessArticle

Explainable Machine Learning Framework for Predicting Auto Loan Defaults

by Shengkun Xie and Tara Shingadia

Risks 2025, 13(9), 172; https://doi.org/10.3390/risks13090172 - 11 Sep 2025

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection [...] Read more.

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection methods, including SHapley Additive exPlanations (SHAP) values and Mutual Information (MI), and resampling techniques such as Synthetic Minority Over-sampling TEchnique (SMOTE), SMOTE-Tomek, and SMOTE Edited Nearest Neighbor (SMOTE-ENN), are evaluated. The results show that combining SHAP-based feature selection with SMOTE-Tomek resampling and a Stacked Classifier consistently achieves superior predictive performance. These findings highlight the value of explainable AI in enhancing credit risk assessment for auto lending. This research also offers valuable insights for addressing other financial modeling challenges involving imbalanced datasets, supporting more informed and reliable decision-making. Full article

(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)

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29 pages, 1977 KB

Open AccessArticle

Evaluating the Decline Registered Auditors Will Have on the Future of the Assurance Industry in South Africa

by Thameenah Abrahams and Masibulele Phesa

Risks 2025, 13(9), 171; https://doi.org/10.3390/risks13090171 - 10 Sep 2025

Abstract

Purpose: This article evaluated the decline of registered auditors (RAs) and its impact on the future of the assurance industry in South Africa. Auditors play a critical role in ensuring the transparency, trust, and credibility of financial statements. The decrease in the [...] Read more.

Purpose: This article evaluated the decline of registered auditors (RAs) and its impact on the future of the assurance industry in South Africa. Auditors play a critical role in ensuring the transparency, trust, and credibility of financial statements. The decrease in the number of registered auditors has become a pressing issue, raising concerns about the assurance industry’s ability to maintain a sufficient number of registered auditors and continue providing assurance services to public and private entities. Methodology: A qualitative Delphi methodology was employed, involving interviews with RAs who are registered with the Independent Regulatory Board for Auditors (IRBA). Eight RAs participated in structured interviews. This approach enabled the researcher to gather expert opinions, identify emerging trends, and explore challenges and opportunities within the audit profession related to the decline of RAs. Main findings: The decline of RAs is straining client demands, increasing workloads, and leading to a shortage of audit firms, which in turn affects audit quality and methodologies. Audit firms struggle to attract and retain talent due to regulatory burdens, economic pressures, and concerns about work–life balance. These pressures have resulted in higher audit fees, increased compliance costs, and more extensive training requirements. Smaller audit firms are especially impacted, with some downscaling their assurance services or exiting the market entirely. Practical implications: This study underscores the pressing need for regulatory bodies, such as the IRBA, to address the challenges faced by audit firms, particularly in terms of compliance and workforce retention. Proactive strategies are required to preserve the quality and accessibility of assurance services. Contribution: This study contributes to the ongoing discourse on the future of the audit profession by offering grounded insights into how the industry might sustain itself amid a declining number of RAs and changing professional dynamics. Full article

(This article belongs to the Special Issue Risks in Finance, Economy and Business on the Horizon in the 2030s)

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19 pages, 765 KB

Open AccessArticle

Digital Financial Literacy and Anxiety About Life After 65: Evidence from a Large-Scale Survey Analysis of Japanese Investors

by Jargalmaa Amarsanaa, Trinh Xuan Thi Nguyen, Yu Kuramoto, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(9), 170; https://doi.org/10.3390/risks13090170 - 8 Sep 2025

Abstract

In the context of Japan’s rapidly aging population, people’s anxiety about life after 65, especially regarding financial sustainability, has become a growing concern. This study examines old age anxiety through the lens of digital financial literacy (DFL), which can significantly impact people’s retirement [...] Read more.

In the context of Japan’s rapidly aging population, people’s anxiety about life after 65, especially regarding financial sustainability, has become a growing concern. This study examines old age anxiety through the lens of digital financial literacy (DFL), which can significantly impact people’s retirement well-being and long-term financial security in today’s digital environment. Drawing on a large-scale dataset from the “Survey on Life and Money,” jointly conducted by Rakuten Securities and Hiroshima University, we analyze responses from 94,695 individuals aged 40 to 64 who are active bank account holders. Based on ordinal logistic regression, our findings reveal a negative association between DFL and old age anxiety. Further analysis of the five dimensions of DFL demonstrates that several practical components, such as digital financial know-how, decision-making abilities, and self-protection skills, are associated with alleviated old age anxiety. In contrast, a reliance on basic financial knowledge and general awareness alone may exacerbate anxiety. These findings underscore the need to move beyond basic digital awareness and focus on promoting practical skills in digital finance, ultimately supporting better financial decision-making and enhancing overall well-being in older age. Full article

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28 pages, 1156 KB

Open AccessFeature PaperArticle

Financial Systemic Risk and the COVID-19 Pandemic

by Xin Huang

Risks 2025, 13(9), 169; https://doi.org/10.3390/risks13090169 - 4 Sep 2025

Viewed by 155

Abstract

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, [...] Read more.

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, Distress Insurance Premium, and SRISK. In the time-series dimension, we use the Dynamic OLS model and find that financial variables, such as credit default swap spreads, equity correlation, and firm size, significantly affect the SRMs, but the COVID-19 variables do not appear to drive the SRMs. However, if we focus on the first wave of the COVID-19 pandemic in March 2020, we find a positive and significant COVID-19 effect, especially before the government interventions. In the cross-sectional dimension, we run fixed-effect and event-study regressions with clustered variance-covariance matrices. We find that market capitalization helps to reduce a firm’s contribution to the SRMs, while firm size significantly predicts the surge in a firm’s SRM contribution when the pandemic first hits the system. The policy implications include that proper market interventions can help to mitigate the negative pandemic effect, and policymakers should continue the current regulation of required capital holding and consider size when designating systemically important financial institutions. Full article

(This article belongs to the Special Issue Systemic Risk in the Financial System: New Developments and Challenges)

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13 pages, 1224 KB

Open AccessArticle

Cryptocurrency Market Dynamics: Copula Analysis of Return and Volume Tails

by Giovanni De Luca and Andrea Montanino

Risks 2025, 13(9), 168; https://doi.org/10.3390/risks13090168 - 2 Sep 2025

Viewed by 378

Abstract

This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially [...] Read more.

This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially in the tails of the distribution. Our findings reveal significant upper and lower–upper tail dependencies, suggesting that extreme trading volumes are associated with both positive and negative return extremes. The results confirm a nonlinear and asymmetric volume–return relationship, which traditional linear models fail to capture. Full article

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18 pages, 1171 KB

Open AccessArticle

Financial Institutions of Emerging Economies: Contribution to Risk Assessment

by Yelena Popova, Olegs Cernisevs, Sergejs Popovs and Almas Kalimoldayev

Risks 2025, 13(9), 167; https://doi.org/10.3390/risks13090167 - 1 Sep 2025

Viewed by 343

Abstract

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and [...] Read more.

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically sound risk assessment formula that is still reliable even in the absence of complete vulnerability data. This is particularly important for financial institutions operating in emerging markets, where regulators rarely provide centralized vulnerability assessments and where Basel-type frameworks are only partially implemented. The contribution of the paper is a practically verified Bayesian network model that integrates threat likelihoods, vulnerability likelihoods, and their impacts within a probabilistic structure. Using 500 stratified Monte Carlo scenarios calibrated to real fintech and banking institutions operating under EU and national supervision, we demonstrate that excluding vulnerability impact from the model does not significantly reduce the predictive performance. These findings advance the theory of risk assessment, simplify practical implementation, and enhance the scalability of risk modeling for both traditional banks and fintech institutions in emerging economies. Full article

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19 pages, 395 KB

Open AccessFeature PaperArticle

Robust Tail Risk Estimation in Cryptocurrency Markets: Addressing GARCH Misspecification with Block Bootstrapping

by Christos Christodoulou-Volos

Risks 2025, 13(9), 166; https://doi.org/10.3390/risks13090166 - 29 Aug 2025

Viewed by 425

Abstract

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in [...] Read more.

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare the performance of classical and block bootstrap procedures in residual risk. Diagnostic tests indicate that standardized residuals are dependent, contrary to the independent and identically distributed (i.i.d.) assumption of conventional FHS. Comparing the block and ordinary bootstrapping approaches, we find that block bootstrap produces wider, more conservative confidence intervals, particularly in extreme tails (e.g., 0.1% and 99.9% percentiles). The findings suggest that block bootstrapping can be employed as a correction instrument in risk modeling where the standard volatility filters do not work. The article highlights the necessity to account for remaining dependencies and offers practical recommendations for more robust tail risk estimation during volatile markets. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

20 pages, 1969 KB

Open AccessArticle

Contagion or Decoupling? Evidence from Emerging Stock Markets

by Lumengo Bonga-Bonga and Zinzile Lorna Ndiweni

Risks 2025, 13(9), 165; https://doi.org/10.3390/risks13090165 - 29 Aug 2025

Viewed by 271

Abstract

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring [...] Read more.

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring the extent of shock spillover between selected developed and emerging markets during idiosyncratic crisis and normal periods. The US and EU are identified as developed economies. However, emerging markets are classified by regions to determine whether their responses to shocks from developed economies are homogeneous or heterogeneous depending on the region to which they belong. The suggested entropy test is based on the conditional correlations obtained from an asymmetric dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (A-DCC GARCH) model. In addition to economic methods, statistical methods based on the regime-switching technique are used to date the different phases of the global financial crisis (GFC) and the European sovereign debt crisis (ESDC). Our findings show that all emerging markets decoupled from developed economies in at least one of the phases of the two crises. These findings provide valuable insights for policymakers, investors, and asset managers for portfolio allocation and financial regulations. Full article

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34 pages, 616 KB

Open AccessArticle

Does ERP Implementation Lower Corporate Financing Costs? A Dual Perspective from Risk Management and Value Creation

by Juanjuan Zhang, Song Zhou and Fuhui Ma

Risks 2025, 13(9), 164; https://doi.org/10.3390/risks13090164 - 27 Aug 2025

Viewed by 549

Abstract

This study examines the impacts of Enterprise Resource Planning (ERP) systems on financing costs from the dual perspectives of risk management and relative value creation based on corporate value maximization objectives. Data were manually collected from the listed companies in China. It is [...] Read more.

This study examines the impacts of Enterprise Resource Planning (ERP) systems on financing costs from the dual perspectives of risk management and relative value creation based on corporate value maximization objectives. Data were manually collected from the listed companies in China. It is found that the equity financing cost and debt financing cost of enterprises implementing ERP systems are both significantly higher than those without, and the impact of the ERP systems on equity financing cost is more significant than on debt financing cost. The endogeneity problems are addressed using the fixed effect, the instrumental variables in the two-stage least squares (2SLS) regression test, and the Heckman two-stage regression test. Further exploration into the underlying reasons for these results through mechanism analysis reveals that ERP systems can systematically and effectively enhance risk management levels and corporate value returns, bringing higher returns for investors and achieving a win-win situation. These research findings fundamentally help alleviate the agency problems between companies and investors, and also explain the advantages of an investment-oriented capital market in resolving conflicts among its various participants. Additionally, heterogeneity analysis further shows that the ownership structure and age structure of enterprises have a significantly negative moderating effect on the above results, and the moderating effect on equity financing cost is stronger than on debt financing cost. Full article

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34 pages, 1917 KB

Open AccessArticle

Enhancing Insurer Portfolio Resilience and Capital Efficiency with Green Bonds: A Framework Combining Dynamic R-Vine Copulas and Tail-Risk Modeling

by Thitivadee Chaiyawat and Pannarat Guayjarernpanishk

Risks 2025, 13(9), 163; https://doi.org/10.3390/risks13090163 - 27 Aug 2025

Viewed by 441

Abstract

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, [...] Read more.

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda. Full article

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28 pages, 802 KB

Open AccessArticle

On the Multi-Periodic Threshold Strategy for the Spectrally Negative Lévy Risk Model

by Sijia Shen, Zijing Yu and Zhang Liu

Risks 2025, 13(9), 162; https://doi.org/10.3390/risks13090162 - 22 Aug 2025

Viewed by 299

Abstract

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for [...] Read more.

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for optimizing corporate capital allocation. Addressing the inadequate adaptability of traditional single-period threshold strategies in time-varying market environments, this paper proposes a dividend strategy based on multiperiod dynamic threshold adjustments. By implementing periodic modifications of threshold parameters, this strategy enhances the risk model’s dynamic responsiveness to market fluctuations and temporal variations. Within the framework of the spectrally negative Lévy risk model, this paper constructs a stochastic control model for multiperiod threshold dividend strategies. We derive the integro-differential equations for the expected present value of aggregate dividend payments before ruin and the Gerber–Shiu function, respectively. Combining the methodologies of the discounted increment density, the operator introduced by Dickson and Hipp, and the inverse Laplace transforms, we derive the explicit solutions to these integro-differential equations. Finally, numerical simulations of the related results are conducted using given examples, thereby demonstrating the feasibility of the analytical method proposed in this paper. Full article

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24 pages, 3300 KB

Open AccessArticle

ETF Resilience to Uncertainty Shocks: A Cross-Asset Nonlinear Analysis of AI and ESG Strategies

by Catalin Gheorghe, Oana Panazan, Hind Alnafisah and Ahmed Jeribi

Risks 2025, 13(9), 161; https://doi.org/10.3390/risks13090161 - 22 Aug 2025

Viewed by 526

Abstract

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their [...] Read more.

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

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14 pages, 1100 KB

Open AccessArticle

Algorithmic Bias Under the EU AI Act: Compliance Risk, Capital Strain, and Pricing Distortions in Life and Health Insurance Underwriting

by Siddharth Mahajan, Rohan Agarwal and Mihir Gupta

Risks 2025, 13(9), 160; https://doi.org/10.3390/risks13090160 - 22 Aug 2025

Viewed by 1015

Abstract

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 [...] Read more.

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring. Full article

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Risks, Volume 13, Issue 9 (September 2025) – 13 articles

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