THE TRAJECTORY OF AI-DRIVEN CREDIT SCORING AND THE REFINEMENT OF LEGAL MECHANISMS FOR A DIGITAL FUTURE (International journal of artificial intelligence, 2025)
This paper examines how modern machine-learning techniques are reshaping consumer credit scoring and what legal-regulatory guardrails are required to keep accuracy accountable. It argues that, in tabular lending contexts, ensemble models (e.g., gradient-boosted trees) deliver meaningful lifts over traditional logistics while remaining compatible with faithful, applicant-level explanations (e.g., SHAP). The core risks are opacity, drift, and disparate impacts—problems that cannot be solved by technical fixes alone but must be governed through clear model-risk controls, documented fairness choices, auditable data lineage, and reason-code specificity. Surveying leading regimes, the paper highlights the EU AI Act’s “high-risk” obligations for creditworthiness assessment, U.S. expectations under ECOA/Reg B and SR 11-7/OCC model-risk guidance, MAS FEAT/Veritas operationalization, and BIS/BCBS prudential concerns about correlated model failures. Against this backdrop, Uzbekistan’s existing statutes on personal data, consumer credit, and credit histories provide an enabling legal scaffold. The paper proposes targeted secondary rules—explainability standards tied to actual computations, ex-ante fairness metrics and monitoring, mandatory drift detection with challenger models, and full vendor oversight—to align innovation with due process. Done well, AI scoring can expand inclusion, strengthen dignity through actionable explanations, and enhance system resilience without trading off consumer protection.