The growth of credit card application needs to be balanced with the anticipation of bad credit risk because it does not use security collateral as warranty. The usage 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 aim of this research is to design a data mining model for credit scoring in bank in order to support and improve the performance of the credit analyst job. The proposed model applies classification using Nave Bayes and ID3 algorithm. The accuracy of Nave Bayes classifier is 82% and ID3 is 76%. So we can conclude that Nave Bayes classifier has better accuracy than ID3 classifier.
Madyatmadja E. D and Aryuni M. (2014). COMPARATIVE STUDY OF DATA MINING MODEL FOR CREDIT CARD APPLICATION SCORING IN BANK. Journal of Theoretical and Applied Information Technology, 59 (2), 269-274.
Credit Scoring, Data Mining, Credit Card, Bank, Nave Bayes, ID3, Classification