Performance Evaluation of Machine Learning Models for Credit Risk Prediction

The purpose of this research paper is to propose an approach for calculating the optimal threshold for predictions generated by binomial classification models for credit risk prediction. Our approach is considering the cost matrix and cumulative profit chart for setting the threshold value. In the paper we examine the performance of several models trained with homogeneous (Random Forest, XGBoost, etc.) and heterogeneous (Stacked Ensemble) ensemble classifiers. Models are trained on data extracted from Lending Club website. Different evaluation measures are derived to compare and rank the fitted models. Further analysis reveals that application of trained models with the set according to the proposed approach threshold leads to significantly reduced default loans ratio and at the same time improves the credit portfolio structure of the Peer-to-Peer lending platform. We evaluate the models performance and demonstrate that with machine learning models Peer-to-Peer lending platform can decrease the default loan ratio by 8% and generate profit lift of 16%.