Credit Default Prediction Based on Blending Learning Model

Abstract

Currently, the preventions of credit default usually will be evaluated by users credit value before loaning from banks. However, for the loan user, who have no existing record of loaning and the situation of low credit value, it cannot precisely recognize the risk of credit default. After a credit default, the bank not only doesnt get the signed compensation and principal in time, but also the debtor needs to bear the expensive corresponding late fees and credit costs. Therefore, reducing credit defaults can decline more burden of debtors and creditors. In this paper, the authors evaluate multiple machine learning models including algorithms belong to machine learning and deep learning, using blending model to boost the prediction effect and accuracy, while proposing an optimization design to further enhance the stability, accuracy and generalization capacity of proposed algorithm, so as to effectively decrease the credit default rate and the risk of bank loss in practice.