IMPACT OF AI-BASED CREDIT SCORING ON LENDING DECISIONS

Abstract Through the detailed comparison of the performance of AI-based credit scoring to that of conventional credit evaluation techniques, this study empirically analyses the influence of AI-enabled credit scoring on the decisions of lending. Differences in decision consistency, approval behaviour, and predictive precision are analysed using comparative modelling and a simulated dataset. According to the results, AI-led credit scoring helps to better differentiate risk, boost lending efficiency, as well as allocate credit properly, especially to borrowers with little credit history. The research also highlights significant problems with equity, openness, and governance. Moreover, the data indicate that responsible oversight and implementation are necessary for the long-term integration of AI-driven credit scoring into contemporary lending systems, despite its many advantages.