Risk Identification Model of Related Loans Based on Analysis of Multi-layer Complex Network Structure

Abstract

Abstract: Related loans refer to loans that are linked together due to a certain (or several) relationship(s) and these loans as a whole are likely to cause group default. Because of the complicated cross-bank loan and concealed influence relationship in related loans, it is difficult for banks to identify related loans and take effective measures. Based on the loan guarantee data of several commercial banks, this paper constructs a multi-layer complex network, then designs an algorithm to identify the related loan structure and analyzes the ef-fectiveness; the default rate of different related loans structures is calculated and compared with 4 risk indicators; finally, the signifi-cance of default risk of different related loans structures is tested. The result shows:(1) the multi-layer network related loan model and the recognition algorithm constructed in this paper greatly improve the efficiency and accuracy of related loan identification, and overcome the dual concealed problem of related loans that cannot be solved by the single layer network; (2) the betweenness and clustering coeffi-cient indicators are more consistent with the true default rate of related loans, while the indicators based on the clearing payment capacity and risk distance fail to predict the true default rate; (3) when there are circle-linked structure and sink structure in related loans net-work, the default risk of loans increases significantly. The model and method constructed in this paper provide a theoretical basis for identifying multi-bank and multi-relationship related loans, and have practical significance for banks to detect risk network structures and to control related loan risks.

Key words: multi-layer network, related loans, structure recognition, risk identification

Zhang Enyong, Liu Chao, Li Yongli, Xia Lijuan. Risk Identification Model of Related Loans Based on Analysis of Multi-layer Complex Network Structure[J]. Management Review, 2024, 36(5): 3-11.

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