Many credit card businesses are no longer profitable due to antiquated and increasingly obsolete methods of acquiring customers, and as importantly, they followed suit when identifying ideal customers. The objective of this study is to identify the high spending and revolving customers through the development of proper parameters. We combined the back propagation neural network, decision tree and logistic methods as a way to overcome each method’s deficiency. Two sets of data were used to develop key eigenvalues that more accurately predict ideal customers. Eventually, after many rounds of testing, we settled on 14 eigenvalues with the lowest error rates when acquiring credit card customers with a significantly improved level of accuracy. It is our hope that data mining and big data can successfully utilize these advantages in data classification and prediction.
Communications in Computer and Information Science, 652, 13-24