Fraud Detection in Medical Insurance Claim System using Machine Learning : A Review

Authors

  • Paresh Gohil  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dr. Sheshang Degadwala  Computer Engineering Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dhairya Vyas  Managing Director, Shree Drashti Infotech LLP, Vadodara, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT228664

Keywords:

Medical Insurance Claim, Support Vector Machine, K-nearest Neighbor, Random Forest, Decision Tree, Navier Bayes.

Abstract

Since the beginning of the insurance industry, there has been the problem of fraudulent insurance claims. These are a broad variety of illegal activities, the most of which are never uncovered while costing the insurance industry billions of dollars annually. It is estimated that India's insurance industry is suffering losses of around 600–Rs. 600 million each year because of India's growing economy, more awareness, and strengthened distribution networks. 800 crores in losses sustained yearly due to bogus claims. India comes up at number 10 for gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result of this, we are presenting a framework for the selection of features to be used in machine learning, which will enable the robust categorization of insurance claims. It will demonstrate how these technologies might be used to the development of a system that can prevent certain kinds of fraud in the field of healthcare. Several different studies have been carried out to demonstrate that the established approach may effectively identify instances of healthcare fraud. As a result, it may be useful in the prevention of false claims and gives greater insight into how to enhance patient management and treatment methods.

References

  1. N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud, “A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement,” IEEE Access, vol. 8, pp. 58546–58558, 2020, doi: 10.1109/ACCESS.2020.2983300.
  2. S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh, and W. C. Hong, “Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward,” IEEE Access, vol. 8, pp. 474–448, 2020, doi: 10.1109/ACCESS.2019.2961372.
  3. M. Bärtl and S. Krummaker, “Prediction of claims in export credit finance: a comparison of four machine learning techniques,” Risks, vol. 8, no. 1, 2020, doi: 10.3390/risks8010022.
  4. L. Ismail and S. Zeadally, “Healthcare Insurance Frauds: Taxonomy and Blockchain-Based Detection Framework (Block-HI),” IT Prof., vol. 23, no. 4, pp. 36–43, 2021, doi: 10.1109/MITP.2021.3071534.
  5. I. Matloob, S. A. Khan, and H. U. Rahman, “Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology,” IEEE Access, vol. 8, pp. 143256–143273, 2020, doi: 10.1109/ACCESS.2020.3013962.
  6. G. Kowshalya and M. Nandhini, “Predicting Fraudulent Claims in Automobile Insurance,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018, no. Icicct, pp. 1338–1343, 2018, doi: 10.1109/ICICCT.2018.8473034.
  7. S. Wang et al., “Blockchain-Powered Parallel Healthcare Systems Based on the ACP Approach,” IEEE Trans. Comput. Soc. Syst., vol. 5, no. 4, pp. 942–950, 2018, doi: 10.1109/TCSS.2018.2865526.
  8. S. Chakraborty, S. Aich, and H. C. Kim, “A Secure Healthcare System Design Framework using Blockchain Technology,” Int. Conf. Adv. Commun. Technol. ICACT, vol. 2019-February, pp. 260–264, 2019, doi: 10.23919/ICACT.2019.8701983.
  9. T. T. A. Dinh, R. Liu, M. Zhang, G. Chen, B. C. Ooi, and J. Wang, “Untangling Blockchain: A Data Processing View of Blockchain Systems,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 7, pp. 1366–1385, 2018, doi: 10.1109/TKDE.2017.2781227.
  10. W. Kozlow, M. J. Demeure, L. M. Welniak, and J. L. Shaker, “Acute extracapsular parathyroid hemorrhage: Case report and review of the literature,” Endocr. Pract., vol. 7, no. 1, pp. 32–36, 2001, doi: 10.4158/ep.7.1.32.
  11. M. Raikwar, S. Mazumdar, S. Ruj, S. Sen Gupta, A. Chattopadhyay, and K. Lam, “2018 9th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2018 - Proceedings,” 2018 9th IFIP Int. Conf. New Technol. Mobil. Secur. NTMS 2018 - Proc., vol. 2018-January, 2018.
  12. R. Roy and K. T. George, “Detecting insurance claims fraud using machine learning techniques,” Proc. IEEE Int. Conf. Circuit, Power Comput. Technol. ICCPCT 2017, 2017, doi: 10.1109/ICCPCT.2017.8074258.
  13. X. Liang, J. Zhao, S. Shetty, J. Liu, and D. Li, “Integrating blockchain for data sharing and collaboration in mobile healthcare applications,” IEEE Int. Symp. Pers. Indoor Mob. Radio Commun. PIMRC, vol. 2017-October, pp. 1–5, 2018, doi: 10.1109/PIMRC.2017.8292361.
  14. F. Tang, S. Ma, Y. Xiang, and C. Lin, “An Efficient Authentication Scheme for Blockchain-Based Electronic Health Records,” IEEE Access, vol. 7, pp. 41678–41689, 2019, doi: 10.1109/ACCESS.2019.2904300.

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Published

2022-12-30

Issue

Section

Research Articles

How to Cite

[1]
Paresh Gohil, Dr. Sheshang Degadwala, Dhairya Vyas, " Fraud Detection in Medical Insurance Claim System using Machine Learning : A Review" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.417-427, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT228664