A Review on Health Insurance Claim Fraud Detection
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.
Keywords
Download Options
Introduction
In several countries fraudulent behavior in health insurance is a major problem. Data mining tools and techniques can be used to detect fraud in large sets of insurance claim data. One of the most common data mining techniques used for finding fraudulent records is anomaly detection.
This paper aims to review various approaches used for Health insurance fraud detection. There are three major parties involved in the entire system,
(1) Service Providers
(2) Insurance Subscribers
(3) Insurance Carriers
The Service Providers including doctors, hospitals, ambulance companies and laboratories. The Insurance Subscribers including patients and patient’s employers. The Insurance Carriers who receive regular premiums from subscribers and pay health care cost on behalf of their subscribers.
There is a difference between fraud prevention and fraud detection. The fraud prevention describes measures to avoid fraud to occur. The fraud detection involves identifying fraud as quickly as possible, once it has been committed.
According to the National Health care anti-fraud association, health care fraud is the misrepresentation of Claims for gaining some shabby benefits. The health industry in India is losing approximately Rs.600 crores on “false claims” every year. So to make health insurance feasible, there is a need to focus on eliminating or reducing fraudulent claims.
Conclusion
In conclusion, this paper reviews two approaches for detecting fraudulent behavior in health insurance claim. By analyzing the aforementioned techniques, we will get a clear idea for the future work in health insurance claim fraud detection. In India, we have three levels of health care network, namely primary, secondary, and tertiary. It provides an opportunity for data miners to use the huge amount of data. The main task is to integrate data from different sources and then put to use by data miners to achieve the desired results.