Credit scoring using genetic programming

Abstract

Growing numbers in e-commerce orders lead to an increase in risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring is employed to identify customers’ default probability. Credit scoring has been widely studied and many different methods in different fields of research have been proposed. The primary aim of this work is to develop a credit scoring model as a replacement for the pre risk check of the e-commerce risk management system risk solution services (rss). The pre risk check uses data of the order process and includes exclusion rules and a generic credit scoring model. The new model is supposed to work as a replacement for the whole pre risk check and has to be able to work in solitary and in unison with the rss main risk check. An application of Genetic Programming to credit scoring is presented. The model is developed on a real world data set provided by Arvato Financial Solutions. The data set contains order requests processed by rss. Results show that Genetic Programming outperforms the generic credit scoring model of the pre risk check in both classification accuracy and profit. Compared with Logistic Regression, Support Vector Machines and Boosted Trees, Genetic Programming achieved a similar classificatory accuracy. Furthermore, the Genetic Programming model can be used in combination with the rss main risk check in order to create a model with higher discriminatory power than its individual models.