Skip Navigation
Oklahoma State University
Research Week

Home of World Class Research

Identifying the Factors Responsible for Loan Defaults using SAS® Enterprise Miner

Identifying the Factors Responsible for Loan Defaults using SAS® Enterprise Miner

Name:
Juhi Bhargava

Department:
Business Analytics

Abstract:
The lending business is crucial to the profitability of a bank or financial institution. Loan defaults, delay in repayment by customers, lead to problems in cash flow position. The last economic crisis in US was triggered by loan defaults. This study aims to identify the factors contributing towards loan defaults, delay in repayments as well as the characteristics of a borrower who will honor all the obligations of a loan. The results will enable us to determine the relationship between loan and customer characteristics and the probability to default. These may also be used to appraise and monitor credit risk at the time of loan approval and subsequently. The loan data for the year 2015 was extracted from the website of Lending Club, an online credit market place. It consists of all loans issued in 2015 along with the loan status. It contains 111 variables such as the details of customer’s loan account, principal outstanding, amount paid, interest rate, length of employment, annual income, loan status– current, default, in grace or late due, purpose of loan and so on. There are 421,095 records. The factors contributing towards loan default will be identified and predicted using models such as logistic regression, decision tree and artificial neural networks. The classification will enable the lending institutions to optimize their policies and strategies to reduce the loan defaults and also to make informed decisions about the current customers at the risk of default. Data Source: https://www.lendingclub.com/info/download-data.action