HEALTH INSURANCE FRAUD DETECTION: THE ROLE OF ADVANCED IT SYSTEMS IN PREVENTING AND IDENTIFYING FRAUD
DOI:
https://doi.org/10.34218/IJCET_16_01_259Keywords:
Insurance, Fraud, AI, ITAbstract
In this study, the usage of advanced IT systems such as AI, machine learning, blockchain and others is studied in the field of detection of health insurance fraud. Results indicate that AI driven fraud detection model improves fraud detection accuracy by a large margin, but decreases their false positives and financial losses. This integration of blockchain has encryption guaranteeing the safety of the data and that of claims processing. The findings imply that AI and blockchain complemented one another to make better fraud prevention in the health insurance sector.
References
Saldamli, G., Reddy, V., Bojja, K. S., Gururaja, M. K., Doddaveerappa, Y., & Tawalbeh, L. (2020, April). Health care insurance fraud detection using blockchain. In 2020 seventh international conference on software defined systems (SDS) (pp. 145-152). IEEE. https://www.researchgate.net/profile/Lakshmi-Narasimhan-Srinivasagopalan/publication/387692115_AI-Enhanced_Fraud_Detection_in_Healthcare_Insurance_A_Novel_Approach_to_Combatting_Financial_Losses_through_Advanced_Machine_Learning_Models/links/6777d1cd117f340ec3f014db/AI-Enhanced-Fraud-Detection-in-Healthcare-Insurance-A-Novel-Approach-to-Combatting-Financial-Losses-through-Advanced-Machine-Learning-Models.pdf
Kapadiya, K., Patel, U., Gupta, R., Alshehri, M. D., Tanwar, S., Sharma, G., & Bokoro, P. N. (2022). Blockchain and AI-empowered healthcare insurance fraud detection: an analysis, architecture, and future prospects. IEEE Access, 10, 79606-79627. 10.1109/ACCESS.2022.3194569
Mazumder, S. A., & Rhaman, M. A. (2024). Patient Care and Financial Integrity In Healthcare Billing Through Advanced Fraud Detection Systems. Academic Journal on Business Administration, Innovation & Sustainability, 4(2), 10-69593. http://dx.doi.org/10.69593/ajbais.v4i2.74
Lu, J., Lin, K., Chen, R., Lin, M., Chen, X., & Lu, P. (2023). Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism. BMC Medical Informatics and Decision Making, 23(1), 62. https://doi.org/10.1186/s12911-023-02152-0
Narne, H. (2024). Machine Learning for Health Insurance Fraud Detection: Techniques, Insights, and Implementation Strategies. https://www.researchgate.net/profile/Harish-Narne-3/publication/386384259_Machine_Learning_for_Health_Insurance_Fraud_Detection_Techniques_Insights_and_Implementation_Strategies/links/674fd8fe790d154bf9c28eb1/Machine-Learning-for-Health-Insurance-Fraud-Detection-Techniques-Insights-and-Implementation-Strategies.pdf
Hamid, Z., Khalique, F., Mahmood, S., Daud, A., Bukhari, A., & Alshemaimri, B. (2024). Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making, 24(1), 112. https://doi.org/10.1186/s12911-024-02512-4
Waghade, S. S., & Karandikar, A. M. (2018). A comprehensive study of healthcare fraud detection based on machine learning. International Journal of Applied Engineering Research, 13(6), 4175-4178. https://api.semanticscholar.org/CorpusID:201048558
Kanksha, Bhaskar, A., Pande, S., Malik, R., & Khamparia, A. (2021). An intelligent unsupervised technique for fraud detection in health care systems. Intelligent Decision Technologies, 15(1), 127-139. https://doi.org/10.3233/IDT-200052
Nabrawi, E., & Alanazi, A. (2023). Fraud detection in healthcare insurance claims using machine learning. Risks, 11(9), 160. https://doi.org/10.3390/risks11090160
Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y. (2020). A secure ai-driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access, 8, 58546-58558. 10.1109/ACCESS.2020.2983300
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