Demystifying deep credit models in e-commerce lending: An explainable approach to consumer creditworthiness

The 'Buy Now, Pay Later' service has revolutionized consumer credit, particularly in e-commerce, by offering flexible options and competitive rates. However, assessing credit risk remains challenging due to limited personal information. Given the availability of consumer online activities, including shopping and credit behaviors, and the necessity for model explanation in high-stakes applications such as credit risk management, we propose an intrinsic explainable model, GLEN (GRU-based Linear Explainable Network), to predict consumers' credit risk. GLEN leverages the sequential behavior processing capabilities of GRU, along with the transparency of linear regression, to predict credit risk and provide explanations simultaneously. Empirically validated on a real-world e-commerce dataset and a public dataset, GLEN demonstrates a good balance between competitive predictive performance and interpretability, highlighting critical factors for credit risk forecasting. Our findings suggest that past credit status is crucial for credit risk forecasting, and the number of borrowings and repayments is more influential than the amount borrowed or repaid. Additionally, browsing frequency and purchase frequency are also important factors. These insights can provide valuable guidance for platforms to predict credit risk more accurately.