FRAUD DETECTION IN HEALTH CARE INSURANCE USING DATA MINING BY INTEGRATING HOSPITAL AND HEALTH INSURANCE SYSTEM

Abstract

Fraud is widely spread and it can be very costly to the health- care insurance system. It
involves intentional deception intended to result in an unauthorized benefit. It is shocking because the
incidence of health insurance fraud increasing every year. In order to detect and avoid the fraud, data
mining techniques are applied. This includes some basic knowledge about health care system and its
behaviors, analysis of the health care insurance data. Data mining is divided in two learning
techniques, supervised and unsupervised learning is employed to detect fraud claims. This technique
has its own advantages and disadvantages, so by combining the advantages of both the techniques a
hybrid approach for detecting fraud claims in healthcare insurance industry is proposed. So, for make
healthcare insurance industry free from fraud, it is necessary to focus on the elimination of fake
claims arriving through health insurance. According to recent survey, it is found that the number of
false claims in the industry is near about 15% of total claims. Insurance companies in USA losses
over 30 billion USD annually to healthcare insurance frauds. The statistics is increased in developing
country like India as well.