Data Mining in Credit Scoring and Future Application

Abstract

To improvise credit rating process, banks have developed models to check credit ratings. Automation is an important chain to screen this process. Credit risk is divided into two categories: “good risk” groups with a high possibility of repaying debts, and “bad risk” groups with a high probability of not doing so. Credit scoring models need to be created using data mining approaches. With help of this approach, financial institutes can track credit records of any person easily. This model may help to identify the key demographic characteristics associated with credit risk by using the past payment information, demographic factors, and statistical techniques. Since it is obviously difficult to analyse this massive amount of data economically and laboriously, data mining methods were used. Each has distinct advantages and disadvantages in comparison to the others. This paper emphasized the role of data mining in credit rating in financial institutes. Research also tries to find future recommendations.

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