A Blockchain and Machine Learning based Framework for Efficient Health Insurance Management

Goyal, Adit; Elhence, Anubhav; Chamola, Vinay; Sikdar, Biplab

doi:10.1145/3485730.3493685

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Also, the paper applied random forest model for predicting the potential risks that can be used in risk-related premium rebate computation. The paper shown that multiclassi cation model and random forest model performed best base on the accuracy obtained [21].…”

Section: Big Datamentioning

confidence: 93%

Artificial Intelligence for Insurance Industry: A Survey

Isa

Fernando

2022

Preprint

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The cost of insurance is rising all over the world, with frequent proposed policies and fraud playing a role. In recent years, AI techniques have been increasingly used for numerous insurance tasks. However, AI researchers frequently struggle technically to understand the vast and sophisticated domain knowledge as well as rapidly changing literature. Although several studies have already been published on the use of artificial intelligence in the context of specific insurance tasks, our work provides a unified general survey of the existing literature, incorporating various AI techniques into all major insurance tasks by providing a systematic literature survey of the rapidly growing literature on artificial intelligence in the insurance industry. From 2017 to the present, we focus our research on machine learning, big data, blockchain, data mining, chatbot theory, and its application in fraud detection, insurance policy, claim prediction, rick prediction, and other areas to systematically introduce existing work in the insurance sector using AI techniques.

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Section: Big Datamentioning

confidence: 93%

Artificial Intelligence for Insurance Industry: A Survey

Isa

Fernando

2022

Preprint

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“…The proposed approach is different from the existing solutions [ 5 , 15 , 24 , 53 , 54 , 55 , 56 ] in the following ways: Existing solutions mainly focus on a particular technology and try to determine its potential in improving the HIN process; Existing solutions discuss the benefits of using BLC in the HIN process, but they do not evaluate the solution due to the limitation of there being less use of BLC in healthcare; The existing studies used a single method of evaluation, while the proposed solution is evaluated using hybrid approach (mathematical modeling, focus group study, and industrial case study), which shows the generalizability of research findings; Existing studies mainly emphasize the adaptability of the HIN process and provide various techniques for it. Furthermore, there exist no solutions that specifically address the end-to-end HIN process cycle security.…”

Section: Framework Evaluationmentioning

confidence: 95%

“…According to Goyal et al, having health insurance is vital for everyone, especially with rising medical expenditures, since medical crises may have a significant financial and emotional effect [ 55 ]. However, the existing insurance system is too costly, and the claim settlement procedure is overly protracted and cumbersome.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study

2023

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Health insurance has become a crucial component of people’s lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP’s applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting.

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“…Paper title [21] Internet of Things A blockchain-based machine learning framework for edge services in IIoT [22] Internet of Things Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach [23] Internet of Things Dynamic access control policy based on blockchain and machine learning for the internet of things [24] Supply Chain A blockchain and machine learning-based drug supply chain management and recommendation system for smart pharmaceutical industry [25] Supply Chain A machine learning based approach for predicting blockchain adoption in supply Chain [26] Medicine A blockchain and machine learning based framework for efficient health insurance management [27] Medicine A novel blockchain-enabled heart disease prediction mechanism using machine learning [28] Medicine Blockchain and machine learning in health care and management [29] Medicine Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data [30] Medicine Healthcare Ledger Management: A Blockchain and Machine Learning-Enabled Novel and Secure Architecture for Medical Industry [31] Finance An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning [32] Security A machine learning approach for blockchain-based smart home networks security [33] Security BChainGuard: A New Framework for Cyberthreats Detection in Blockchain Using Machine Learning [34] Security Efficient privacy-preserving machine learning for blockchain network…”

Section: Refs Domainmentioning

confidence: 99%

“…Goyal et al [26] aimed to create a framework that leverages blockchain technology and machine learning for the health insurance sector, which is both quick and economical.…”

Section: Blockchain and Machine Learning In Medicinementioning

confidence: 99%

Blockchain meets machine learning: a survey

Kayikci,

Khoshgoftaar

2024

J Big Data

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Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.

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A Blockchain and Machine Learning based Framework for Efficient Health Insurance Management

Cited by 7 publications

References 11 publications

Artificial Intelligence for Insurance Industry: A Survey

Artificial Intelligence for Insurance Industry: A Survey

Towards a Secure Technology-Driven Architecture for Smart Health Insurance Systems: An Empirical Study

Blockchain meets machine learning: a survey

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