Enhancing Credit Risk Assessment Using Machine Learning: A Case Study on Early Payment Risk Prediction

Abstract

The financial sector, especially the banking industry, is significantly advancing in evaluating credit risk. While useful, old-style credit risk measurement tools like FICO are somewhat limited and cause problems when quickly evolving markets and changing consumer behavior occur. The advent of machine learning (ML), which is data-driven and adaptive in its approach, will do real-time risk analysis and have better predictions. This paper aims to explore the application of ML in credit risk management with particular reference to early payment risk prediction on consumer credit. Unlike a typical model, the ML models give a more detailed and adaptive evaluation of the consumers by combining behavioral, transactional, and external data. Using models like random forests, SVM, and XGboost, institutions can deal with the nonlinear relationships of each variable. This research reveals the effectiveness of the proposed methods, outperforming traditional methods in identifying early payments, with up to 20-30% enhanced accuracy of the ML models. Other issues identified include data privacy, model interpretability, and implementation issues, which are also described below. Lastly, incorporating ML into tackling credit risk assessment is a critical improvement in financial risk management. It provides a better way of managing risk, decision-making, and dynamics within unstable markets.