Abstract
The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing fraud detection methods can be divided into two categories: pattern based and outlier detection based methods. However, because of the sparsity and complex granularity of big data, these methods have high false positive in fraud detection. In this paper, we propose an effective hybrid fraud detection method. We propose SSIsomap which improves isomap to cluster behaviors into behavior classes and propose SimLOF which improves LOF to conduct outlier detection, then we use Dempster-Shafer evidence Theory for combining behavior pattern evidence and outlier evidence, which yields a degree of belief of fraud to the new coming claim. The experiment result shows our method has significantly higher accuracy than exsiting methods in medical insurance fraud detection.
Supported by the National Natural Science Foundation of China under Grant No.61303005, the Science and Technology Development Plan Project of Shandong Province No. 2014GGX101019 and No. 2014GGX101047, the Fundamental Research Funds of Shandong University No.2014JC025, the National Key Technologies R&D Program No.2012BAH54F02 and No.2012BAH54F04.
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Sun, C., Li, Q., Cui, L., Yan, Z., Li, H., Wei, W. (2015). An Effective Hybrid Fraud Detection Method. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_51
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DOI: https://doi.org/10.1007/978-3-319-25159-2_51
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