Machine Learning Approaches for Credit Risk Evaluation in Digital Lending Platforms

Abstract

Online lending has transformed the modern-day credit markets by facilitating quick, automated lending through high amounts of borrower information. Although traditional credit scoring methods continue to be popular because of their transparency and their familiarity to the regulators, they tend to be inadequate in capturing the nonlinear and high-dimensional behavioural traits associated with digital lending environments. Simultaneously, machine learning methods have become realistic applications in credit risk assessment, and they show quantifiable improvements in prediction accuracy in various consumer credit data. The paper will provide a summarized and evidence-based analysis of machine learning solutions in credit risk assessment in online lending systems. Based on open dataset, and known benchmark research, the work incorporates architectural and design aspects, assessment techniques and comparative performance lessons. An architecture of reference system in line with the operational lending processes is introduced and the trade-offs between performances, robustness and interpretability of the operational lending processes are discussed. The discussion points out the possibilities and limitations to the real implementation of machine learning in regulated credit decision systems.