1. Customers’ Risk Type Prediction Based on Analog Complexing.
- Author
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He, Changzheng, Zhu, Bing, Zhang, Mingzhu, Zhuang, Yuanyuan, He, Xiaoli, and Du, Dongyue
- Subjects
RISK assessment ,CREDIT cards ,CONSUMER behavior ,ARTIFICIAL neural networks - Abstract
Credit card holder's behaviour may change over time, which would lead to the change of risk type. This paper introduces a new method, AnalogComplexing (AC), to predict consumer's risk type. Furthermore, the new method uses the observed historical process itself for prediction which does not need any information of input variables in unknown prediction period. The authors applied the new proposed method AC to a bank customer dataset from one city of Western China, and the empirical study shows that AC is significantly better than widely-used neural network in terms of prediction accuracy. The empirical results in indicates that AC is an effective method, and provide a new way to predict consumers’ risk type. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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