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 March 2026 | Viewed by 2

Special Issue Editors


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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

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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

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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

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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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Published Papers

This special issue is now open for submission.
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