Work

Research on Measuring Default Risk of Internet Microloans Based on the CreditRisk+ Model

Year: 2024

Type: conference-paper

Abstract: With the rapid growth of internet finance, fintech companies are increasingly entering the "Internet + Consumer Finance" sector, offering personal consumer loans. Effectively preventing and managing credit risk from borrower defaults has become crucial for small loan businesses. This paper analyzes the credit risks faced by personal microloans in China's internet finance sector, using M Fintech Company's loan data as a case study. A binary logistic regression model is constructed to predict default probabilities, and the Credit-Risk+ model is applied to measure credit risk, improving calculation accuracy. Empirical research validates the model's effectiveness, and the influence of borrower characteristics on credit risk is discussed. This study aids microloan companies in risk control and management improvement, while also serving as a reference for related research.

Source: International Conference on Computer Science and Management Technology (ICCSMT)

DOI: 10.1145/3708036.3708149

Authors: Zhonghua Liu, Shuang Zhang, Chaohui Xie

Institutions: Beijing University of Chemical Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4406668034

FWCI: 1.73

Cites: 11

Cited by: 1

Related to: 10