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An Enhanced Credit Risk Evaluation by Incorporating Related Party Transaction in Blockchain Firms of China

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  • Ying Chen

    (School of Management and Engineering, Nanjing University, 22 Hankou Road, Nanjing 210093, China
    Nanjing Institute of Digital Financial Industry Co., Ltd., 6 Tianpu Road, Nanjing 211899, China)

  • Lingjie Liu

    (Treasury Department, CITIC Group Corporation, 10 Guanghua Road, Beijing 100020, China)

  • Libing Fang

    (School of Management and Engineering, Nanjing University, 22 Hankou Road, Nanjing 210093, China)

Abstract

Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.

Suggested Citation

  • Ying Chen & Lingjie Liu & Libing Fang, 2024. "An Enhanced Credit Risk Evaluation by Incorporating Related Party Transaction in Blockchain Firms of China," Mathematics, MDPI, vol. 12(17), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2673-:d:1465986
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    References listed on IDEAS

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