Credit Risk Management with Alternative Data: Expanding the Predictive Frontier Beyond Traditional Scoring Models

Abstract

Credit risk management is fundamental to the survival of financial institutions and the stability of the broader financial system. While traditional credit scoring models like FICO serve as industry standards in consumer credit, they have notable limitations: they exclude individuals without conventional credit histories and rely on static historical data, which fails to capture dynamic changes in borrowers’ financial behaviour and risk profiles. This study addresses two core questions: (1) How can alternative features, derived from borrowers’ controllable financial behaviour, be constructed to capture risk information missed by FICO? (2) How can the marginal contribution of these features to credit risk identification be quantified? Using the Lending Club loan dataset, we adopt a framework of “feature selection → model building → performance evaluation → robustness testing,” constructing a baseline model using only the FICO score and an extended model that incorporates alternative features via the XGBoost algorithm with 5-fold crossvalidation. Results indicate that the extended model achieves robust improvements in key metrics (AUC, KS, F1), effectively bridging gaps in dynamic risk detection. Alternative features supplement traditional models by identifying high-risk segments, particularly those with “high debt, low stability, and no assets.” Academically, this study advances credit risk identification methodologies and enriches the theoretical application of alternative data; practically, it offers financial institutions enhanced risk control tools to reduce nonperforming loans.