Loan Default Prediction
By: Giang Vu
Department: Mathematics
Faculty Advisor: Dr. Tao He
Banks make a major revenue from lending loans. There are various types of loans that serve different purposes, such as personal loan, auto loan, student loans, mortgage loans, home equity loans, credit-builder loan, debt consolidation loan, and payday loan. Their customers are borrowers who ask for credit loans from the bank with the agreement that they can make periodic payments with interest on time to repay the credit amount. However, not every customer is capable of complying the agreement. Failing to make required debt payments results in loan default, one of major risks that causes huge losses to both banks and customers. Therefore, it is critically important for banks to conduct investigations on their customers’ background and history before authorizing a new loan. In this project, the goal is to analyze a dataset that contains customers’ information in the year of 2019 by using different statistical model methods and figure out the best approaches for the banks to predict and identify risks in the most accurate way.