Data Mining Method for Comparative Analysis of Personal Loans Based on Principal Component Analysis, Naive Bayes and Decision Tree

Abstract

The data mining classification techniques and analysis can enable banks to move precisely classify consumers into various credit risk group. Knowing what risk group a consumer falls into would allows a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued on terms commensurate with the risk of default. So research en for classification and prediction of loan grants. The attributes are determined that have greatest effect in the loan grants. For this purpose C4.5, CART and Naïve Bayes are compared and analyzed in this research. This concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.