Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: An Analysis, Architecture, and Future Prospects

Khyati Kapadiya, Usha Patel, Rajesh Gupta, Mohammad Dahman Alshehri, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Nowadays, health insurance has become an essential part of people's lives as the number of health issues increases. Healthcare emergencies can be troublesome for people who can't afford huge expenses. Health insurance helps people cover healthcare services expenses in case of a medical emergency and provides financial backup against indebtedness risk. Health insurance and its several benefits can face many security, privacy, and fraud issues. For the past few years, fraud has been a sensitive issue in the health insurance domain as it incurs high losses for individuals, private firms, and governments. So, it is essential for national authorities and private firms to develop systems to detect fraudulent cases and payments. A high volume of health insurance data in electronic form is generated, which is highly sensitive and attracts malicious users. Motivated by these facts, we present a systematic survey for Artificial Intelligence (AI) and blockchain-enabled secure health insurance fraud detection in this paper. This paper presents a taxonomy of various security issues in health insurance. We proposed a blockchain and AI-based secure and intelligent system to detect health insurance fraud. Then, a case study related to health insurance fraud is presented. Finally, the open issues and research challenges in implementing the blockchain and an AI-empowered health insurance fraud detection system is presented.

Original languageEnglish
Pages (from-to)79606-79627
Number of pages22
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • AI
  • Healthcare insurance
  • blockchain
  • fraud detection
  • security

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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