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An Enhanced TSA-MLP Model for Identifying Credit Default Problems

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  • Jianhua Jiang
  • Xianqiu Meng
  • Yang Liu
  • Huan Wang

Abstract

Credit default has always been one of the critical factors in the development of personal credit business. By establishing a default identification model, default can be avoided effectively. There are some existing methods to identify credit default. However, these methods have some problems: Problem (1): It is different to deal the non-linear data, Problem (2): The local stagnation results in the high error rate, and Problem (3): The premature convergence leads to the low classification rate. In this paper, the sinhTSA-MLP default risk identification model is proposed to solve these problems. In this model, the proposed sinhTSA method can effectively avoid the problems of falling into local optimum and premature convergence. And the benchmark test results demonstrate sinhTSA is superior to other methods. According to the two experiments, the classification rate reaches 77.35% and 96.48%. Therefore, the sinhTSA-MLP default identification model has some particular advantages in identifying credit problems The feasibility of the sinhTSA-MLP default identification model has been proved through helping to manage credit default more consciously.

Suggested Citation

  • Jianhua Jiang & Xianqiu Meng & Yang Liu & Huan Wang, 2022. "An Enhanced TSA-MLP Model for Identifying Credit Default Problems," SAGE Open, , vol. 12(2), pages 21582440221, April.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221094586
    DOI: 10.1177/21582440221094586
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    1. Chenxiang Zhang & Fengrui Zhang & Ningyan Chen & Huizhen Long, 2022. "RETRACTED ARTICLE: Application of artificial intelligence technology in financial data inspection and manufacturing bond default prediction in small and medium-sized enterprises (SMEs)," Operations Management Research, Springer, vol. 15(3), pages 941-952, December.

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