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AI and Machine Learning for Credit Risk and Financial Distress...
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AI and Machine Learning for Credit Risk and Financial Distress Prediction

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 6047

Special Issue Editors

Dr. Mohammad Mahdi Mousavi

E-Mail Website
Guest Editor
School of Management, University of Bradford, Bradford BD7 1DP, UK
Interests: bankruptcy prediction models; credit scoring; corporate finance; applications of artificial intelligence and machine learning in finance; FinTech
Prof. Dr. Jamal Ouenniche

E-Mail Website
Guest Editor
Business School, University of Edinburgh, Edinburgh EH8 9JS, UK
Interests: design and performance evaluation of models and methodologies for forecasting levels and volatilities of prices of strategic commodities; bankruptcy; consumer behaviour
Dr. Stefano Grillini

E-Mail Website
Guest Editor
Morgan Stanely, London E14 4QA, UK
Interests: stress testing and portfolio risk; investment strategies and factor-based portfolio optimization

Special Issue Information

Dear Colleagues,

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) is reshaping how financial institutions, investors, and regulators assess credit risk and anticipate financial distress. The fusion of advanced algorithms with rich and diverse data sources—ranging from traditional financial statements to alternative datasets such as transaction histories, online behavior, and macroeconomic indicators—offers unprecedented potential to improve accuracy, timeliness, and fairness in decision-making.

This Special Issue seeks to bring together high-quality research that explores both methodological innovations and practical implications. We invite contributions from scholars, practitioners, and policymakers that advance the theory, application, and evaluation of AI/ML models in credit scoring, default risk, bankruptcy forecasting, and financial stability analysis.

Topics of interest include, but are not limited to:

  • Novel AI/ML architectures and hybrid models for predictive analytics in credit risk;
  • Integration of macroeconomic, ESG, and alternative data into credit scoring models;
  • Explainable and interpretable AI for regulatory compliance and trust in automated systems;
  • Comparative analyses of AI/ML versus traditional econometric approaches;
  • Ethical, fairness, and policy implications of AI-driven credit decision-making.

We welcome empirical studies, methodological papers, and review articles that bridge finance, data science, and policy research.

Dr. Mohammad Mahdi Mousavi
Prof. Dr. Jamal Ouenniche
Dr. Stefano Grillini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • credit risk
  • credit scoring
  • financial distress prediction
  • bankruptcy prediction
  • predictive analysis
  • alternative data
  • explainable AI
  • risk management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

20 pages, 1595 KB  
Article
Explainable AI for Financial Distress: Evidence from Market Volatility and Regime Dynamics
by Seyed Jalal Tabatabaei and Mohammad Mahdi Mousavi
J. Risk Financial Manag. 2026, 19(5), 348; https://doi.org/10.3390/jrfm19050348 - 11 May 2026
Viewed by 82
Abstract
This study investigates the role of market volatility, proxied by the CBOE Volatility Index (VIX), as a potential regime-dependent interaction of corporate leverage risk within the S&P 100. Addressing the limitations of traditional financial distress models in capturing non-linear and regime-dependent dynamics, we [...] Read more.
This study investigates the role of market volatility, proxied by the CBOE Volatility Index (VIX), as a potential regime-dependent interaction of corporate leverage risk within the S&P 100. Addressing the limitations of traditional financial distress models in capturing non-linear and regime-dependent dynamics, we employ XGBoost combined with SHAP-based explainable AI (XAI) on a longitudinal dataset spanning 2000–2025. The results show that Total Debt remains the dominant predictor of financial distress, while the predictive contribution of risk-related variables such as the VIX and equity returns increases during crisis periods. Monetary policy indicators become more important during pandemic conditions, whereas inflation dominates in a stable environment. This finding highlights the regime-dependent nature of financial risk drivers and demonstrates the value of explainable machine learning in developing interpretable risk diagnostic frameworks. By integrating predictive accuracy with interpretability, this study provides new insights into the non-linear interaction between firm-level leverage and external market volatility. Full article
(This article belongs to the Special Issue AI and Machine Learning for Credit Risk and Financial Distress Prediction)
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27 pages, 495 KB  
Article
Hierarchical Fuzzy Cognitive Maps for Financial Risk Monitoring Using Aggregated Financial Concepts
by George A. Krimpas, Georgios Thanasas, Nikolaos A. Krimpas, Maria Rigou and Konstantina Lampropoulou
J. Risk Financial Manag. 2026, 19(3), 219; https://doi.org/10.3390/jrfm19030219 - 16 Mar 2026
Viewed by 656
Abstract
This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory [...] Read more.
This study addresses the gap between predictive optimization and monitoring-oriented risk concentration by introducing a hierarchical Fuzzy Cognitive Map (FCM) framework for financial risk assessment. Financial distress prediction models are employed to estimate firm-level default probabilities and are required to comply with regulatory standards. IFRS 9 and Basel III/IV frameworks emphasize model explainability, scenario analysis and causal transparency, which are essential for compliance purposes. The methodology aggregates correlated financial ratios into financial concepts through unsupervised clustering. Concepts interact through a learned coupling matrix and a controlled multi-step propagation, which enables the amplification of risk signals. A small residual correction is applied at the final readout, preserving the interpretability of the proposed framework. The framework was applied to two severely imbalanced benchmark bankruptcy datasets. It achieved higher precision–recall performance than Logistic Regression (PR–AUC ≈0.32 vs. 0.27), improved calibration (Brier score ≈0.046 vs. 0.089) and maintained competitive Recall@Top–K under tight supervisory monitoring budgets. Hierarchical FCM achieved predictive performance comparable to nonlinear models while maintaining concept-level interpretability. Our findings demonstrate that structured concept aggregation combined with interaction-based propagation provides a transparent alternative to purely predictive black-box models in financial distress assessment and is aligned with regulatory frameworks. Full article
(This article belongs to the Special Issue AI and Machine Learning for Credit Risk and Financial Distress Prediction)
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44 pages, 1431 KB  
Article
Balancing Fairness and Accuracy in Machine Learning-Based Probability of Default Modeling via Threshold Optimization
by Essodjolo Kpatcha
J. Risk Financial Manag. 2025, 18(12), 724; https://doi.org/10.3390/jrfm18120724 - 17 Dec 2025
Viewed by 2705
Abstract
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingly prevalent in financial decision-making, concerns around bias and transparency have [...] Read more.
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingly prevalent in financial decision-making, concerns around bias and transparency have grown, particularly when improvements in fairness are achieved at the expense of predictive performance. To mitigate these issues, we propose a model-agnostic, post-processing threshold optimization framework that adjusts classification cut-offs using a tunable parameter, enabling institutions to balance fairness and performance objectives. This approach does not require model retraining and supports a scalarized optimization of fairness–performance trade-offs. We conduct extensive experiments with logistic regression, random forests, and XGBoost, evaluating predictive accuracy using Balanced Accuracy alongside fairness metrics such as Statistical Parity Difference and Equal Opportunity Difference. Results demonstrate that the proposed framework can substantially improve fairness outcomes with minimal impact on predictive reliability. In addition, we analyze model-specific trade-off behaviors and introduce diagnostic tools, including quadrant-based and ratio-based analyses, to guide threshold selection under varying institutional priorities. Overall, the framework offers a scalable, interpretable, and regulation-aligned solution for deploying responsible credit risk models, contributing to the broader goal of ethical and equitable financial decision-making. Full article
(This article belongs to the Special Issue AI and Machine Learning for Credit Risk and Financial Distress Prediction)
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17 pages, 432 KB  
Article
Normalizing Pandemic Data for Credit Scoring
by Joseph L. Breeden
J. Risk Financial Manag. 2025, 18(11), 657; https://doi.org/10.3390/jrfm18110657 - 20 Nov 2025
Cited by 1 | Viewed by 1490
Abstract
The COVID-19 pandemic created abnormal credit risk conditions that did not align well with pre-2020 credit scores. Since the pandemic, most organizations have either excluded the period 2020–2021 from their modeling or included it without adjustment, leaving it as noise in the data. [...] Read more.
The COVID-19 pandemic created abnormal credit risk conditions that did not align well with pre-2020 credit scores. Since the pandemic, most organizations have either excluded the period 2020–2021 from their modeling or included it without adjustment, leaving it as noise in the data. Model validators and examiners have been divided about requiring one of these approaches or defaulting to model developer judgment. None of this is ideal from a model development perspective. This paper presents a unique technical solution that allows for the inclusion of pandemic data while constructing credit scores and actually produces scores that perform better and have long-term stability across the entire economic cycle. This result negates the common belief that credit scores must be frequently rebuilt in order to maintain rank order accuracy. This analysis uses lifecycle and environment outputs from an Age-Period-Cohort analysis as fixed offsets to credit score development. Panel data is used, so the credit score is developed with a discrete time survival model approach. Logistic regression and stochastic gradient boosted regression trees were tested as estimators with the panel data and APC inputs. Full article
(This article belongs to the Special Issue AI and Machine Learning for Credit Risk and Financial Distress Prediction)
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