Clues from networks: quantifying relational risk for credit risk evaluation of SMEs

Jingjing Long (), Cuiqing Jiang (), Stanko Dimitrov () and Zhao Wang ()
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Jingjing Long: Hefei University of Technology
Cuiqing Jiang: Hefei University of Technology
Stanko Dimitrov: University of Waterloo
Zhao Wang: Hefei University of Technology

Financial Innovation, 2022, vol. 8, issue 1, 1-41

Abstract: Abstract Owing to information asymmetry, evaluating the credit risk of small- and medium-sized enterprises (SMEs) is difficult. While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs, our study considers both intrinsic and relational risks generated by neighbor firms’ publicly available risk events. We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’ credit risk evaluation. Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type, risk event type, and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk. Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs, providing valuable managerial and practical implications for financial institutions.

Keywords: SMEs; Credit risk evaluation; Interfirm network; Risk event; Relational risk (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40854-022-00390-1

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