Design of a Bdp Tool Using Data Mining Techniques

Abstract

Now a day’s most of the financial organizations facing a major problem to recover the money from the borrowers, it becomes the frightening to banks in some situations. As a financial intermediary, one of its roles is to reduce lending risks. Bank lending is an art as well as a science. Success depends on techniques used, knowledge and on an aptitude to assess both credit-worthiness of a potential borrower and the merits of the proposition to be financed. In recent years, banks have increasingly used credit-scoring techniques to evaluate the loan applications they receive from consumers financial institutions always utilized the rules or principles built by the analysts to decide whom to give credit. In order to overcome these difficulties while recovering money the financial institutions and researchers have been developed various credit scoring models but they many not exactly fix in the situation like predicting the borrower attitude. Even though they are following rules and principles while lending money, they are unable to recover the loans from all the borrowers. In order to overcome these types of potential problems, as a precautionary measure, a software tool can be developed using Data Mining techniques aiming at giving qualitative and useful guidelines to the financial institutions while making the decision of money lending. This proposed work is aimed at designing a software tool to facilitate the effective money lending process by automating the prediction of customer attitude towards the money management and automation of decision making process.

Key words: money lending, customer attitude, software tool, automation of decision making, data mining techniques