What should lenders be more concerned about? Developing a profit-driven loan default prediction model

Reliable and effective loan default risk prediction can help regulators and lenders effectively identify risky loan applicants and develop proactive and timely response measures to enhance the stability of the financial system. Traditional prediction models concentrate more on improving loan default prediction accuracy, while neglecting to take profit maximization as the goal and evaluation measure of model construction. In this study, a novel profit-driven prediction model is proposed, taking a profit indicator as the optimization objective of the Bayesian optimization to optimize the hyperparameters of the predictor-categorical boosting. The Shapley additive ex-planations (SHAP) value is then calculated to further interpret the relationship between the input variables and the predicted values. Based on two datasets from Renrendai and Lending Club, the experimental results and statistical test indicate that the proposed model achieves the highest profit-related evaluation metrics values, with the mean average extra profit rate values of 3.0872% and 2.1858% respectively, and the mean Profit values of 5168.8762 and 352.9787 in two datasets respectively. SHAP value further reveals the key factors that will impact predictive output, which provides more valuable information for platforms and lenders for identifying possible defaulters.

Automated summary

Reliable and effective loan default risk prediction is crucial for the stability of the financial system. This study proposes a profit-driven prediction model that optimizes hyperparameters and calculates SHAP values for improved accuracy and interpretability. Experimental results show that the model achieves high profit-related evaluation metrics values on different datasets.