Comparison of Data Mining Algorithms in Credit Card approval

The use of credit scoring can be used to help the credit risk analysis in determining the applicant's eligibility. Data mining has been proven as a valuable tool for credit scoring. The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on comparing several algorithms used by Weka, which is viewed as one of the most promising paradigms of computational intelligence. The aim of this paper is to evaluate data mining using Tree based algorithm, Rule based and Bayesian networks and see how either the data or the algorithms can be improved to enhance the output percentages of the algorithms.