Artificial Intelligence in Credit Risk Assessment: A Comparative Study of Machine Learning Models versus Traditional Credit Scoring in Retail Lending
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This study evaluates the transition from traditional logistic-regression scorecards to advanced machine learning (ML) models in retail credit risk assessment. As financial institutions increasingly adopt data-driven cultures to navigate macroeconomic uncertainty, a central question emerges: do ML algorithms provide sufficient predictive and inclusive advantages to justify their ethical and regulatory complexities? Utilizing a comparative secondary-research methodology, this paper synthesizes evidence from a massive academic benchmark (Lessmann et al., 2015), real-world regulatory reporting on Upstart's AI underwriting model (CFPB), and a comprehensive analysis of 26.2 million credit-bureau records (Albanesi & Vamossy, 2024). The findings demonstrate that ensemble methods, particularly random forests, significantly outperform traditional models in discriminatory accuracy, achieving up to a 0.171 AUC advantage. In applied settings, AI-driven underwriting expanded credit access by 27% to 43.4% while substantially lowering average interest rates. Furthermore, ML models proved highly effective in reducing misclassification (by 41%), with the most significant accuracy gains observed among low-income, young, and minority borrowers. However, integrating these predictive "black box" frameworks into corporate business culture introduces severe challenges regarding algorithmic transparency, ethical governance, and trust. The study concludes that while ML offers transformative improvements in credit access and risk prediction, financial institutions must balance these technological advantages with rigorous internal controls, deep business expertise, and transparent regulatory compliance to ensure equitable and responsible lending.
Keywords
Artificial Intelligence (AI), Credit Risk, Credit Scoring, Financial Inclusion, Logistic Regression, Machine Learning, Random Forest, Retail Lending