A Review of AI Credit Scoring in Consumer Finance: Risks, Fairness and Governance

Abstract

 This study examines the application of artificial intelligence (AI) in credit scoring within consumer finance, focusing on its potential benefits, risks, and governance implications. Compared with traditional credit scoring models, AI-based systems can process larger and more diverse datasets, identify complex patterns, and improve the efficiency and accuracy of credit risk assessment. However, their use also raises important concerns, including algorithmic bias, lack of transparency, data privacy risks, and challenges in regulatory oversight. Drawing on existing literature and publicly available case studies, the study discusses how AI credit scoring may affect financial inclusion, consumer protection, and institutional decision-making. It further highlights the importance of explainable AI, fairness assessment, and sound governance frameworks to ensure responsible adoption. Although the study does not conduct empirical analysis with real-world consumer finance data, it provides a conceptual understanding of the opportunities and limitations of AI credit scoring. Future research should compare traditional and AI-based models using real datasets and examine how evolving regulations shape their practical application across different markets.