Using Neural Networks to Classify the Personal Loan Applicants

Abstract

Traditional methods of deciding whether to grant credit to a particular individual use human judgment of the risk of default based on experience of previous decisions. However, economic pressures resulting from increased demand for credit, allied with greater commercial competition and the emergence of new computer technology have led to development of sophisticated statistical models to aid the credit granting decision making process. Credit scoring is the name used to describe this process of determining how likely applicants are to default with their repayments. Credit scoring has some obvious benefits that have led to its increasing use in loan evaluation. For example, it is quicker, cheaper and more objective than judgmental method. A wide range of statistical methods such as discriminant analysis, logistic regression, and neural networks have been applied for credit scoring. In this paper, we design a neural network credit scoring system for classifying the applicants of personal loans in bank and compare the performance of this model with discriminant analysis and logistic regression models. The results of this investigation show that the neural network model is more accurate and more flexible than discriminant analysis and logistic regression.

Keywords

• credit scoring
• discriminant analysis
• logistic regression
• scoring model
• neural network