Research on Efficiency in Credit Risk Prediction Using Logistic-SBM Model

Abstract

Network lending, an innovative financial lending product, is separated from traditional financial media and implemented on the Internet platform. We study the credit risk prediction of online loan based on risk efficiency analysis. Moreover, we put forward the concept of borrower risk efficiency and apply it to risk prediction. The main task of this study is to establish risk efficiency characteristics on the basis of referring to various risk characteristics and carry out risk prediction after passing the screening of a series of features. The framework is realized by combining logistic regression and slack-based measure (SBM), and feature selection and verification are carried out through machine learning and statistics. Firstly, the efficiency risk characteristics are extracted and the risk efficiency is calculated by MaxDEA. Secondly, the features are screened and verified by Python. Then, the efficiency value obtained by SBM method is used as a new index for the training and testing of logistic model together with the initial related indexes. Moreover, in order to prove the effectiveness of the proposed credit risk prediction control scheme based on risk efficiency, the research compares the prediction before and after adding the risk efficiency feature. The simulation results demonstrated that the logistic-SBM model is more suitable for credit risk prediction than the commonly used logistic method, which realized the efficient prediction of credit risk based on the logistic-SBM model. Finally, some suggestions are put forward to China’s regulatory authorities and the platform itself to control the credit risk of Internet lending industry.