A predictive intelligence system of credit scoring based on deep multiple kernel learning

Banks face the task of improving the accuracy in predicting the behavior of individuals who utilize credit cards, as issuing cards to an appropriate applicant is considered an important matter. Credit card debt that is overdue by at least six months has seen a rapid increase in the past decade in the Chinese credit card industry. The problem of delinquency in credit cards not only affects the development of credit cards but it also influences the sustainability of banks. The use of credit risk assessment is critical in providing a solid foundation upon which the credit card issuer can appropriately approve an applicant for a credit card. In previous studies, a variety of machine learning methods have been proposed to assess credit risk. However, conventional methods are viewed as shallow models and are not good at representing compositional features. Thus, this study applies a deep multiple kernel classifier as a state-of-the-art technique, which is proficient in coping with deep structure and complex data in credit risk assessment. It will support decision-makers issuing credit cards in China appropriately. The results indicate that deep multiple kernel classifier outperforms conventional and ensemble models. Credit card departments with better risk management can avoid possible bad debt, hence benefiting banks' operations. The applications of predictive intelligence enhance the prediction of human behavior in the credit card industry. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study demonstrates that the deep multiple kernel classifier outperforms conventional and ensemble models in credit risk assessment, helping credit card issuers appropriately approve applicants for credit cards, improving risk management, avoiding bad debt, and benefiting banks.