Data analysis with different variables and credit risk assessment

Abstract

Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the overdue probability to find some specific associations. Specifically, we performed ANOVA analysis and found that married people borrowed significantly more than other groups, and the delinquency rate of people with higher education was lower, and the delinquency rate of married people was higher than that of unmarried people. In addition, we conducted a binary logistic regression and found that gender had no significant impact on the prediction results, but an individuals amount of bill statement, amount of previous payment, past repayment situation and Amount of the given credit had an impact on the prediction results. Other variables, such as marital status and education, can also impact the predicted results. Our research puts forward more factors affecting credit risk and also different angles that can be used to analyzes individual credit risk. This has a guiding role for financial firms like banks and other companies in the financial industry, providing more ways to help them analyze the credit risk of borrowers.