ClaimChain: Secure Blockchain Platform for Handling Insurance Claims Processing

Bhamidipati, Naga Ramya; Vakkavanthula, Varsha; Stafford, George; Dahir, Masrik; Neupane, Roshan Lal; Bonnah, Ernest; Wang, Songjie; Murthy, J. V. R.; Hoque, Khaza Anuarul; Calyam, Prasad

doi:10.1109/blockchain53845.2021.00019

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…One possible reason is that such risk management can mitigate large-scale risk losses, increasing the fund's rollover balance and safeguarding fairness in emergency responses to risk events. Additionally, risk management serves as a deterrent, minimizing fraudulent claims in the Medicare fund and enhancing fund efficiency, aligning with management philosophies found in the literature (30,31). Other contributing factors may also play a role.…”

Section: Discussionmentioning

confidence: 77%

Research on the risk governance of fraudulent reimbursement of patient consultation fees

Sun,

Wang,

Zhang

et al. 2024

Front. Public Health

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BackgroundThe fundamental medical insurance fund, often referred to as the public’s “life-saving fund,” plays a crucial role in both individual well-being and the pursuit of social justice. Medicare fraudulent claims reduce “life-saving money” to “Tang’s monk meat”, undermining social justice and affecting social stability.MethodsWe utilized crawler technology to gather textual data from 215 cases involving fraudulent health insurance claims. Simultaneously, statistical data spanning 2018 to 2021 was collected from the official websites of the China Medical Insurance Bureau and Anhui Medical Insurance Bureau. The collected data underwent comprehensive analysis through Excel, SPSS 26.0 and R4.2.1. Differential Auto-Regressive Moving Average Model (ARIMA (p, d, q)) was used to fit the fund safety forecast model, and test the predictive validity of the forecast model on the fund security data from July 2021 to October 2023 (the fund security data of Anhui Province from September 2021 to October 2023).ResultsThe outcomes revealed that fraudulent claims by health insurance stakeholders adversely impact the equity of health insurance funds. Furthermore, the risk management practices of Medicare fund administrators influence the publication of fraudulent claims cases. Notably, differences among Medicare stakeholders were observed in the prevalence of fraudulent claims. Additionally, effective governance of fraudulent claims risks was found to have a positive impact on the overall health of healthcare funds. Moreover, the predictive validity of the forecast model on the national and Anhui province’s fund security data was 92.86% and 100% respectively.ConclusionWe propose four recommendations for the governance of health insurance fraudulent claims risk behaviors. These recommendations include strategies such as “combatting health insurance fraudulent claims to preserve the fairness of health insurance funds”, “introducing initiatives for fraud risk governance and strengthening awareness of the rule of law”, “focusing on designated medical institutions and establishing a robust long-term regulatory system”, and “adapting to contemporary needs while maintaining a focus on long-term regulation”.

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Section: Discussionmentioning

confidence: 77%

Research on the risk governance of fraudulent reimbursement of patient consultation fees

Sun,

Wang,

Zhang

et al. 2024

Front. Public Health

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“…The authors demonstrated that ClaimChain is a promising solution and also a cutting-edge alternative for the NICB/ISO database architecture. 94 The authors proposed a deep-learning-based scheme called DeepCoin, used to detect network attacks and fraudulent transactions in the Blockchain-based Energy network. 96 In this, Blockchain is combined with DL using a truncated BackPropagation through Time (BPTT) for the smart grid.…”

Section: Resultsmentioning

confidence: 99%

“…The authors demonstrated that ClaimChain is a promising solution and also a cutting‐edge alternative for the NICB/ISO database architecture 94 . Presented a model for application‐level security built with ML models like K‐Nearest Neighbors (KNN), Logistic Regression (LR), XGBoost and Random Cut Forest (RCF) and assessed using various performance metrics.…”

Section: Resultsmentioning

confidence: 99%

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

Chithanuru

Ramaiah

2023

Concurrency and Computation

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SummaryRecently, Blockchain cryptographic distributed transaction ledger technology finds its usage in many applications. The application's ledgers implemented through Blockchain, ensures tamper‐proof transactions, and in turn the applications became robust enough against cyber‐attack But still adversaries put forward their efforts in detecting the vulnerabilities in the infrastructure to execute their ill intent. In the literature, many counter measures techniques are presented to address the security breaches on the Blockchain. Detecting as well mitigating from the possible anomalies against on blockchain infrastructure through AI techniques is the greatest attempt of this article, and which is much needed now. Hence, this review article enlightens the readers with the essence of cyber security, the security aspects of Blockchain, its infrastructure vulnerabilities, various Blockchain‐enabled use cases along with the their challenges. Primarily, anomaly detection on Blockchain infrastructure through Artificial Intelligence Techniques is focused. A detailed analysis of Artificial Intelligence Techniques in detecting the anomalies with the help of Blockchain and also how these two technologies complement each other was demonstrated with the help of suitable use cases. The merits, challenges along with the possible future directions, while integrating Blockchain with Artificial Intelligence Techniques are presented for the benefit of research community.

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“…Blockchains can be utilized to secure many functions in the insurance domain. They can be used to store the information of policyholders and claim histories in an immutable manner, which smooth and automate the insurance claim processing while being resistant to data integrity attacks as stakeholders verify tamper-proof data [130]. Furthermore, blockchain and smart contracts can be utilized to prevent insurance fraud, as blockchains provide a framework to store insurance data in a manner that cannot be manipulated by unauthorized third parties [131].…”

Section: Insurancementioning

confidence: 99%

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Wijesekara,

Gunawardena

2023

Network

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Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

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ClaimChain: Secure Blockchain Platform for Handling Insurance Claims Processing

Cited by 9 publications

References 22 publications

Research on the risk governance of fraudulent reimbursement of patient consultation fees

Research on the risk governance of fraudulent reimbursement of patient consultation fees

An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions – A review

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

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