Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Data mining techniques are greatly used in the banking industry which helps them compete in the market and provide the right product to the right customer with less risk. Credit risks which account for the risk of loss and loan defaults are the major source of risk encountered by banking industry. Data mining techniques like classification and prediction can be applied to overcome this to a great extent. In this paper we introduce an effective prediction model for the bankers that help them predict the credible customers who have applied for loan. Decision Tree Induction Data Mining Algorithm is applied to predict the attributes relevant for credibility. A prototype of the model is described in this paper which can be used by the organizations in making the right decision to approve or reject the loan request of the customers.

Keywords:- NBFC, Finance Industry, Data Mining; Risk Management; Credit Scoring; Non-Performing Assets; Default Detection; Non Performing Loans Decision Tree; Credit Risk Assessment; Classification; Prediction