Default Prediction for Wholesale and Retail Small and Medium-Sized Enterprises Using Loan Transaction and Market Evaluation Data

Irregular loan transaction behaviors and poor market situation indicate potential risk for wholesale and retail small and medium-sized enterprises (SMEs) in loan use and operation, but the value of these risk information is rarely explored and utilized. This paper innovatively proposed to mine such information to solve the difficulty of default prediction for wholesale and retail SMEs and enrich the credit risk research of special groups. The experimental results showed that tracking loan transaction and market evaluation data can improve the accuracy and recall of the models by an average of 1.38% and 7.66%, respectively, and identify default samples 233.22 days in advance on average, which can provide a new perspective for financial institutions to predict the default risk.