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

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.