IWSL Model: A Novel Credit Scoring Model With Interpretable Features for Consumer Credit Scenarios

Abstract

Current studies have designed many credit scoring models with high performance, but they are often weak in interpretability with obvious “black box” features. This makes them difficult to meet the requirements of the regulators about the model's interpretability. This paper presents a novel credit scoring model as the IWSL model, which is data feature driven with interpretable features. The IWSL model first calculates the representative eigenvectors of default and nondefault samples according to their spatial distribution characteristics, as well as the eigenvector located in the middle of these two types of eigenvectors in the sample space. It then calculates the weighted distance between each sample and each eigenvector to divide the training dataset into three subsets and constructs sublogistic models separately. In the absence of prior information about the optimal weight setting of each attribute, the swarm intelligence algorithm is applied to back-optimize the weights according to the model's generalization ability in the validation stage. The empirical results show that the IWSL model outperforms 12 leading credit scoring models on three public consumer credit scoring datasets with statistical significance. Model component validity testing confirms the effectiveness of the IWSL model's core settings, while sensitivity analysis validates its ability to maintain robust results.