Health Care Insurance Fraud Detection Using Blockchain
Publication Date
4-1-2020
Document Type
Conference Proceeding
Publication Title
2020 Seventh International Conference on Software Defined Systems (SDS)
DOI
10.1109/SDS49854.2020.9143900
First Page
145
Last Page
152
Abstract
The health care industry is one of the important service providers that improves people lives. As the cost of the healthcare service increases, health insurance becomes the only way to get quality service in case of an accident or a major illness. As health insurance will reduces the costs and provides financial and economic stability for an individual. One of the main tasks of healthcare insurance providers is to monitor and manage the data and to provide support to customers. Due to regulations and business secrecy, insurance companies do not share the patient's data but since the data are not integrated and not in sync between insurance providers, there has been an increase in the number of fraud's occurring in healthcare. Often times ambiguous or false information is provided to health insurance companies in order to make them pay for some false claims to the policy holders. The individual policyholder may also claim benefits from multiple insurance providers. There is a financial loss of billions of dollars each year as estimated by the National Health Care Anti-Fraud Association (NHCAA). In order to prevent health insurance fraud, it is necessary to build a system to securely manage and monitor insurance activities by integrating data from all the insurance companies. As blockchain provides an immutable data maintaining and sharing, we propose a blockchain based solution for health insurance fraud detection.
Keywords
Blockchain, Ethereum, fraud detection, Healthcare
Department
Computer Engineering
Recommended Citation
Gokay Saldamli, Vamshi Reddy, Krishna S. Bojja, Manjunatha K. Gururaja, Yashaswi Doddaveerappa, and Loai Tawalbeh. "Health Care Insurance Fraud Detection Using Blockchain" 2020 Seventh International Conference on Software Defined Systems (SDS) (2020): 145-152. https://doi.org/10.1109/SDS49854.2020.9143900