Credit Modeling with Behavioral Data

Sammanfattning: In recent years, the Buy Now Pay Later service has spread across the e-commerce industry, and credit modeling is inevitable of interest for related companies to predict the default rate of the customers. The traditional data used in such models are financial bureaus which include credit records bought from external financial institutions. However, external financial bureaus are not ensured high quality, are expensive , and a large number of the population could lack bank records in some markets. In terms of ethics, the financial bureau can lead to discrimination between the traditional asset holder and the young generation, as well as the developed and developing countries for an international company. Instead of comparing different classification methods, this paper investigates the feasibility and usage of click behavior(CB) data from the customer in credit modeling by carrying out feature engineering and conducting comparative experiments. The study demonstrates whether and how we can use CB data as a new data source and the restrictions. The results show that despite the CB data doesn’t impact enhancing the performance of the traditional model, the CB data model has sufficient performance for orders with CB data and weak performance for orders in general due to the hitting rate of the CB data. The CB not only has predictability on orders placed in the shopping app but also on orders placed from other sources such as the website for the same customer. Besides, the CB data perform better on specific customer segments, including new customers, shopping app customers, and high order amount customers. Adding such segment indicators can improve the performance of the CB model. In addition, the best click behavioral feature set is selected by using correlation analysis and the Reverse Feature Elimination method.