A Review on Health Insurance Claim Fraud Detection

Abstract— A Review on Health Insurance Claim Fraud Detection. 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: Hidden Markov Models, Non Negative Matrix Factorization

Click here to Download Full Paper

____________________________________________________________

Health Insurance Claim Fraud Detection

AD Publications is a rapidly growing academic publisher in the fields of Engineering, Medical-Health, Environmental Science and Agriculture Research. AD Publications is a registered organization broad-based open access and publishes most exciting researches with respect to the subjects of our journals. The Journals is being indexed and abstracted by all major global current awareness and alerting services.
The organization aims at undertaking, co- coordinating and promoting research and development. It provides professional and academic guidance in the field of basic education, Higher Education as well in the Technical Education. Our Aims is to Promote and support, High Quality basic, Scientific Research and development in fields of Engineering, Medical-Health, Environmental Science and Agriculture Research and to Generate Public awareness, provide advice to scholar’s researchers and communicate research outcomes.

Some Important Links About Research Journal
International Journal
Agriculture Journal
Medical Journal
Environmental Journal
Engineering Journal

Translate »