Credit Scoring with Machine Learning Supported by E-Commerce Data

With the rapid growth of e-commerce, the need for credit in e-commerce has increased. E-commerce platforms require high performance as a competitive advantage in their activities.  Traditional credit risk models need improvement to sustain the performance expected by e-commerce platforms. In this study, we investigate alternative behavioral and transactional variables obtained from an e-commerce platform. We examine whether these variables improve the predictive performance of credit risk models beyond traditional financial data. Our research is based on a real e-commerce environment where a machine learning based credit scoring system was implemented. The study focuses on developing and evaluating a credit risk system that integrates platform specific behavioral data, such as shopping frequency, payment methods, Buy Now Pay Later (BNPL) repayment behavior, and wallet usage, with traditional financial and Credit Bureau(CB) indicators. Our findings demonstrate a significant improvement in model discrimination and Gini performance. The localized AI-driven credit scoring system achieved a low-cost, fast, and more accurate credit assessment.Order