Analyzing Credit Defaulter Behavior for Precise Credit Scoring

Abstract

Credit Score is a numerical expression which analyzes the creditworthiness of any individual. Credit scoring becomes important as it help the bank workers to estimate whether the individual qualifies for a loan or not. The bank workers receive many credit applications on daily basis it is very much difficult to analyze this huge amount of data in terms of both economic as well as manpower hence researcher should be interested here in checking the creditability of the applicant using recent data mining and machine learning techniques. The aim of this study is focused on techniques that are used to check the probability of the defaulter credit applicant as well as the method by which the accuracy of the system could be increased. In this paper, various machine learning classification techniques along with artificial neural network has been applied to identify the best technique that could be used for credit scoring.