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Credit strategy research based on Markov random field and enterprise association knowledge graph

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  • Lei Zhang
  • Qiankun Song
  • Meihao Fan
  • Xinyi Yao

Abstract

This paper investigates banks' credit strategies concerning micro, small and medium enterprises (MSMEs) by taking into consideration the intrinsic characteristics of individual enterprises and external correlations among them when assessing credit risk. We analysed transaction data between enterprises and utilised the Bean Search algorithm and Markov random field to construct enterprise network topology diagrams, quantifying inter-enterprise correlations. We also applied the combined weight method to balance their creditworthiness and strength indices and developed a comprehensive and scientifically grounded credit strategy model for MSMEs by employing genetic algorithms to solve nonlinear programming problems. Overall, our proposed model aims to maximise loan revenues while simultaneously minimising the risk of customer default.

Suggested Citation

  • Lei Zhang & Qiankun Song & Meihao Fan & Xinyi Yao, 2024. "Credit strategy research based on Markov random field and enterprise association knowledge graph," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(5), pages 844-857, April.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:5:p:844-857
    DOI: 10.1080/00207721.2023.2300149
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