Research On Campus Loans' Credit Risk Assessment With Logistic Regression Model

As a new product in the field of consumer financial products,the emergence of campus loans is a supplement and improvement to the consumer financial market.Before the emergence of campus loans,P2P network lending in the market was a popular consumer financial product that solved the short-term funding needs of individuals and SMEs.The spending power of student groups has always played an important role in major consumer markets,but the market has not met the consumer financial products of college students.In the war where the P2P network lending platform occupies the market,seizing the student community has become the consensus of all major platforms.The biggest difference between the general P2P network lending platform and the campus lending platform is the difference in funding requirements.Because of this difference,the performance of credit loans for campus loan borrowers is also very different.Therefore,the credit risk assessment of campus loans has certain research value.This article takes the fund demand side of the campus loan as the object of research and analysis.From the perspective of domestic and foreign studies on the credit risk of P2P network lending.Combining the credit risk characteristics of domestic general P2P network lending and the operating model of campus lending,the factors influencing the credit risk of campus credit are explored.In the empirical aspect,Logistic regression analysis was used to analyze the relevant indicators of loan borrowers on campus and analyzing the indicators that have a significant impact on the credit risk of the loan borrowers on campus.This article uses Python data capture technology for data collection,In-depth excavation of public information on top-of-the-line mortgage platforms such as Mingxiao loans and Jiurong Finance.Using these data as a research sample of campus loans to analyze the influencing factors of credit risk on campus loans.Based on this,it puts forward some suggestions on the credit of the campus loan to the borrower and the platform to control the credit risk.