An analytical approach to credit risk assessment using machine learning models

This study presents a novel Early Warning System for monitoring the credit risk of commercial customers at a large international bank headquartered in the Netherlands. Traditional early warning methods often rely on backward-looking indicators such as probability of default or loss given default, which can limit predictive performance. To address this, we investigate the effectiveness of a Watchlist-based trigger for forecasting financial distress and adverse customer migration. We assess its precision, timeliness, and sensitivity across different client status transitions. Using a rich dataset combining internal banking records and external financial information, we implement and compare several machine learning algorithms, including Linear Discriminant Analysis, Logistic Regression, Decision Trees, Support Vector Machines, Random Forest, Gradient Boosting, Extreme Gradient Boosting, and Artificial Neural Networks. To enhance model transparency and support adoption, we employ SHapley Additive exPlanations to interpret key predictors of risk. Among all models, Random Forest achieves the highest performance, demonstrating strong F1 scores, superior trigger precision, and high sensitivity to migration. It successfully anticipates 12.7% of negative client transitions and helps prevent 67.6% of cases that would otherwise result in financial losses for the bank. This research contributes a data-driven, explainable solution for proactive credit risk management and offers actionable insights to support strategic decision-making in commercial banking.