An construction method of scorecard using machine learning and logical regression

Abstract

Scorecard is the main method used by financial institutions to quantitatively assess customer credit risk. The traditional scorecard mainly uses the logical regression (LR) for modeling, although it is good in interpretation and stability, it is not suitable for processing large-scale samples, and its accuracy is low. Meanwhile, with the further study in recent years, machine learning has gradually begun to be applied to high-dimensional large-scale sample modeling in the financial field. However, machine learning also has problems such as poor interpretability and weak generalization ability. This paper proposes to build an integrated model of machine learning and logical regression, and makes full use of the advantages of the two algorithms to develop a new scorecard model. The practice shows that the new scorecard model has good differentiation ability.