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Improving Fraud Detection Mechanism in Health Insurance Industry using Data Mining and Statistical Techniques


Pravin R. Bagde , Department of CSE, PBCE Nagpur, India; Manoj S. Chaudhari, Department of CSE, PBCE Nagpur, India


Mediclaim Fraud; Data Mining; Supervised; Unsupervised; Hybrid; Statistical


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. The data mining goals achieved and functions performed in these approaches have given in this paper. In order to detect and avoid the fraud, data mining techniques are applied. Data mining which is divided into two learning techniques viz., supervised and unsupervised is employed to detect fraudulent claims. But, since each of the above techniques has its own set of advantages and disadvantages, by combining the advantages of both the techniques, a novel hybrid approach for detecting fraudulent claims in health insurance industry is proposed.

Other Details

Paper ID: IJSRDV4I50563
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 908-912

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