MEM: a new mixed ensemble model for identifying frauds

Zhenhua Chen, Weili Jiang, Lei Ma, Junpeng Zhang, Jinshan Hu, Yan Xiang, Dangguo Shao

Abstract: In the social security system, there still exist wilful insurance frauds. In this paper, to address the insufficient stability and randomness of the traditional insurance fraud evaluation model, we propose a new classifier called mixed ensemble model (MEM). Based on the principle of ensemble learning, MEM combines several different individual learners and uses Q statistical methods to evaluate diversity. MEM has been tested on two fraud related datasets to compare with three state-of-the-art classifiers: neural network, naive Bayes and logistic regression. The experimental results show that MEM performs better than the other three classifiers in both datasets under the four measures: accuracy, recall, F-value and kappa. MEM can be a useful method for the detection of social insurance fraud.