An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques

Namrata Ghuse¹ , Pranali Pawar² , Amol Potgantwar³

¹ Computer Engineering Department, SITRC (Pune University), Nashik, India.
² Computer Engineering Department, SITRC (Pune University), Nashik, India.
³ Computer Engineering Department, SITRC (Pune University), Nashik, India.

Correspondence should be addressed to: pranalipawar491@gmail.com.

Section:Research Paper, Product Type: Journal
Vol.5 , Issue.3 , pp.27-33, Jun-2017

Online published on Jun 30, 2017

Copyright © Namrata Ghuse, Pranali Pawar, Amol Potgantwar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Nowadays there is huge amount of data stored in real world databases and this amount continues to grow fast. 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. Fraud and abuse on medical claims became a major concern for health insurance companies last decades. Fraud involves intentional deception or misrepresentation intended to result in an unauthorized benefit. It is shocking because the incidence of health insurance fraud keeps increasing every year. Data mining which is divided into two learning techniques viz., supervised and unsupervised is employed to detect fraudulent claims. Basically random forest algorithm and logistics regression algorithm techniques are used for fraud detection in health insurance. Data mining automatically filtering through immense amounts of data to find known/unknown patterns bring out valuable new perceptions and make predictions.

Key-Words / Index Term :
Data Mining; Random Forest Algorithm; Health Insurance Fraud; Supervised; Unsupervised; Clustering; Logistics Regression Algorithm

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