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Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

Author

Listed:
  • Miao Zhu

    (School of Statistics, Huaqiao University, Xiamen 361021, China
    Institute of Quantitative Economics, Huaqiao University, Xiamen 361021, China)

  • Ben-Chang Shia

    (AI Development Center, Fu Jen Catholic University, New Taipei City 242, Taiwan
    Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Meng Su

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

  • Jialin Liu

    (School of Medicine, Xiamen University, Xiamen 361005, China
    National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China)

Abstract

Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services.

Suggested Citation

  • Miao Zhu & Ben-Chang Shia & Meng Su & Jialin Liu, 2024. "Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk," Mathematics, MDPI, vol. 12(10), pages 1-19, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1582-:d:1397350
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    References listed on IDEAS

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