Automatic Recommender System of Development Platforms for Smart Contract-Based Health Care Insurance Fraud Detection...

Area of Science:

• Blockchain Technology
• Health Informatics
• Cybersecurity

Background:

• Rising health care insurance fraud necessitates automated detection methods.
• Current manual auditing is insufficient for increasing patient volumes.
• Blockchain offers immutability, transparency, and smart contracts for secure health care solutions.

Purpose of the Study:

• To develop smart contracts for efficient health care insurance fraud detection.
• To create a decision map recommender system for selecting optimal blockchain platforms.
• To establish a taxonomy of blockchain platforms for health care applications.

Main Methods:

• Developed smart contracts for 12 identified fraud scenarios.
• Created a taxonomy of 102 blockchain platforms based on application domains.
• Utilized a decision map recommender system to select top blockchain platforms.

Main Results:

• Hyperledger Fabric identified as the best blockchain platform for health care insurance fraud detection.
• The recommender system successfully selected Hyperledger Fabric and Neo.
• A taxonomy classified 102 platforms by suitability for various application domains.

Conclusions:

• The decision map recommender system effectively identified Hyperledger Fabric and Neo for fraud detection.
• Hyperledger Fabric demonstrated superior performance over Neo in fraud detection metrics.
• Implemented a novel smart contract-based system for health care insurance fraud detection.