Multiple Instance Learning for Credit Risk Assessment with Transaction Data

Dec. 1, 2018

Tao ZHANG, Wei ZHANG, Wei XU, Haijing, HAO. Knowledge-Based Systems, Volume 161, 1 December 2018, Pages 65-77.


The present study proposes a comprehensive assessment method that incorporates both conventional data, such as individual socio-demographic information and loan application information, and data for the applicant's dynamic transaction behavior. Our method is based on Radial Basis Function (RBF) Multiple Instance Learning (MIL), which extracts features from a person's transaction behavior history. Five real-world datasets from two large commercial banks in China are used to validate the effectiveness of our proposed method. The experimental results show that our method remarkably improves the prediction performance by using the most commonly used model evaluation criteria.

Related Content