Evaluating the credit risk of SMEs using legal judgments

作者:

Highlights:

• Loan default prediction model with judgments brings economic benefits to a bank.

• Compared with the baseline model, the AUC of model with judgments is 4.6% higher.

• Not all judgments affect credit risk of SMEs.

• The relative value of judgment amount influences predicting credit risk of SMEs.

摘要

Loan application assessments of small and medium-sized enterprises (SMEs) are difficult because of information asymmetry. To mitigate the information asymmetry, this paper focuses on information found in legal judgments involving the company and its principles and combines this information with financial and firm-specific information to help evaluate the credit risk of SMEs. We propose a framework to identify legal judgments that are effective in predicting credit risk and extract relevant features that are contained within the effective legal judgments. Empirical evaluation shows that features extracted from effective legal judgments significantly improve the discrimination performance and granting performance of our model compared with the baseline model, which uses financial and firm-specific features only.

论文关键词:Credit risk,Information asymmetry,Legal judgment,SME,Text mining

论文评审过程:Received 5 December 2019, Revised 15 July 2020, Accepted 15 July 2020, Available online 22 July 2020, Version of Record 28 July 2020.

论文官网地址:https://doi.org/10.1016/j.dss.2020.113364