Credit analysis using data mining techniques in banking sector

Abstract

In the financial market, banking sector is one of the major sectors. The main objective of a bank is to maximize their shareholders returns. While maximizing the shareholders returns, they have to bear number of risks. Credit risk is one of their major risks. Credit risk is the risk that the bankers have to bear when they give loan facilities to the customers. Deciding whether the borrower is suitable to get the loan is such a long process. Currently this process is a manual process in the banks and the final decision is based on the credit officers’ opinions. This study has focused on to analyze the credit analysis of businesses using data mining techniques. Basic aim of this study is to sought and to analyze the best data mining techniques which can be used to credit analysis and appraisals of businesses in banking sector in order to get the accurate decisions by minimizing human errors. . In this study it is empirically evaluated current techniques which are using for credit appraisals and the best data mining techniques which can be used to minimize the human errors in the banking sector. The sample consisted of 1500 records taken from a private bank in Sri Lanka which gives loan facilities to Small and Medium Scale Enterprises.