Performance, Fairness, and Explainability in AI-Based Credit Scoring: A Systematic Literature Review

The integration of artificial intelligence (AI) in the financial sector has seen a rapid increase over the past few years, offering new possibilities to streamline processes while ensuring profitability for lending institutions. With its data-driven capability, predicting the creditworthiness of applicants has demonstrated strong predictive performance, particularly for thin-file clients. Despite these advances, growing concerns regarding AI’s fairness, explainability, and regulatory accountability have increasingly limited its adoption in high-stakes credit decision-making. This paper presents a synthesis derived from a systematic literature review (SLR) of 43 peer-reviewed studies published between 2020 and 2025, focusing on AI-based credit scoring and addressing at least one of the performance, fairness, or explainability dimensions. Eligible studies were limited to peer-reviewed journal and conference articles (2020–2025) retrieved from IEEE Xplore, Scopus, Web of Science, and ScienceDirect (last searched: 30 September), examining AI-driven credit scoring in consumer or lending decision contexts. Guided by the Relevance, Rigor, Reproducibility, and Quality (3Rs&Q) appraisal framework, the review analyzes how existing approaches navigate the interplay among performance, fairness, and explainability under regulatory and human oversight considerations. The findings indicate that these dimensions are predominantly addressed in isolation, with limited attention to their joint treatment in regulated deployment settings. By consolidating empirical and conceptual evidence, this review provides actionable guidance for designing and deploying credit scoring models in practice.