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An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

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  • Shaona Hua

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Chunying Zhang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China)

  • Guanghui Yang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China)

  • Jinghong Fu

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Zhiwei Yang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Liya Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China)

  • Jing Ren

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

Abstract

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

Suggested Citation

  • Shaona Hua & Chunying Zhang & Guanghui Yang & Jinghong Fu & Zhiwei Yang & Liya Wang & Jing Ren, 2024. "An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection," Mathematics, MDPI, vol. 12(11), pages 1-18, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1695-:d:1404862
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    References listed on IDEAS

    as
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    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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