Transforming credit risk assessment: A systematic review of AI and machine learning applications

Jewel Kumar Roy ,László Vasa

doi:10.24294/jipd9652

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Transforming credit risk assessment: A systematic review of AI and machine learning applications

Jewel Kumar Roy

László Vasa

Journal of Infrastructure Policy and Development 2025, 9(1), 9652; https://doi.org/10.24294/jipd9652

Submitted：15 Oct 2024

Accepted：18 Nov 2024

Published：21 Jan 2025

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Abstract

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

Keywords

professional and technical personnel training

JEL Classification

C45; C55; C61; G17; G21; G32

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