Utilizing Blockchain and Smart Contracts for Enhanced Fraud Prevention and Minimization in Health Insurance through Multi-Signature Claim Processing
<|reference_start|>Utilizing Blockchain and Smart Contracts for Enhanced Fraud Prevention and Minimization in Health Insurance through Multi-Signature Claim Processing: Healthcare insurance provides financial support to access medical services for patients while ensuring timely and guaranteed payment for providers. Insurance fraud poses a significant challenge to insurance companies and policyholders, leading to increased costs and compromised healthcare treatment and service delivery. Most frauds, like phantom billing, upcoding, and unbundling, happen due to the lack of required entity participation. Also, claim activities are not transparent and accountable. Fraud can be prevented and minimized by involving every entity and making actions transparent and accountable. This paper proposes a blockchain-powered smart contract-based insurance claim processing mechanism to prevent and minimize fraud in response to this prevailing issue. All entities patients, providers, and insurance companies actively participate in the claim submission, approval, and acknowledgment process through a multi-signature technique. Also, every activity is captured and recorded in the blockchain using smart contracts to make every action transparent and accountable so that no entity can deny its actions and responsibilities. Blockchains' immutable storage property and strong integrity guarantee that recorded activities are not modified. As healthcare systems and insurance companies continue to deal with fraud challenges, this proposed approach holds the potential to significantly reduce fraudulent activities, ultimately benefiting both insurers and policyholders.<|reference_end|>
@article{amin2024utilizing,
    title={Utilizing Blockchain and Smart Contracts for Enhanced Fraud Prevention
  and Minimization in Health Insurance through Multi-Signature Claim Processing},
    author={Md Al Amin, Rushabh Shah, Hemanth Tummala, and Indrajit Ray},
    journal={arXiv preprint arXiv:2407.17765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17765},
    primaryClass={cs.CR}
}