Abstract— The anomaly or outlier
detection is one of the applications of data mining. The major use of anomaly
or outlier detection is fraud detection. Health care fraud
leads to substantial losses of money each year in many countries. Effective fraud
detection is important for reducing the cost of Health care system. This paper reviews
the various approaches used for detecting the fraudulent activities in Health
insurance claim data. The approaches reviewed in this paper are Hierarchical
Hidden Markov Models and Non Negative Matrix Factorization. The data mining
goals achieved and functions performed in these approaches have given in this
paper.
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