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An enterprise financial credit risk measurement method based on differential evolution algorithm

Author

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  • Lixia Du
  • Xin An

Abstract

In order to reduce the time cost and risk misjudgement rate of financial information risk measurement, this paper proposes a new enterprise financial credit risk measurement method based on differential evolution algorithm. Firstly, after preprocessing the enterprise financial credit risk data and determining the location of the clustering centre, a differential evolution automatic clustering model is constructed. Secondly, according to the clustering results, the differential evolution algorithm is used to measure the basic process of enterprise financial credit risk. Finally, the improved differential evolution algorithm is used for iterative measurement to achieve enterprise financial credit risk data measurement. The experimental results show that the time cost of the proposed method for enterprise financial credit risk measurement can be controlled within 0.4 s, and the error rate is not more than 1% under the condition of 1,000 data.

Suggested Citation

  • Lixia Du & Xin An, 2025. "An enterprise financial credit risk measurement method based on differential evolution algorithm," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 24(1/2), pages 67-77.
    • Handle: RePEc:ids:ijitma:v:24:y:2025:i:1/2:p:67-77
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