A survey on machine learning techniques for fraud detection in healthcare

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    An exponential upward change in fraud occurrence has resulted in billions of dollars loss in the world economy. Newer techniques in fraud detection in healthcare domain are continuously evolving and are put into practice in many business fields. In healthcare Fraud detection, user behavior is monitored to analyze and find any suspicious or undesirable behavior and to avoid the same. Undesirable behavior could be anything like crime, fraud, unwarranted intrusion or any other kind of default. The goal of this paper is to provide a comprehensive review of different types of fraud and fraud detection techniques used in last two decades.



  • Keywords

    Fraud; Fraud Detection Techniques; Healthcare; Machine Learning Techniques.

  • References

      [1] M. P. Pawar, “Review on Data Mining Techniques for Fraud Detection in Health Insurance,” vol. 3, no. 2, pp. 1128–1131, 2016.

      [2] McKinsey-CII, “India Health care: Inspiring possibilities, challenging journey,” no. December, p. 34, 2012.

      [3] V. Rawte and G. Anuradha, “Fraud detection in health insurance using data mining techniques,” in 2015 International Conference on Communication, Information & Computing Technology (ICCICT), 2015, pp. 1–5. https://doi.org/10.1109/ICCICT.2015.7045689.

      [4] N. J. Morley, L. J. Ball, and T. C. Ormerod, “How the detection of insurance fraud succeeds and fails,” Psychol. Crime Law, vol. 12, no. 2, pp. 163–180, 2006. https://doi.org/10.1080/10683160512331316325.

      [5] N. Laleh and M. Abdollahi Azgomi, “A taxonomy of frauds and fraud detection techniques,” Commun. Comput. Inf. Sci., vol. 31, pp. 256–267, 2009. https://doi.org/10.1007/978-3-642-00405-6_28.

      [6] The State of Insurance Fraud Technology,” no. November 2016.

      [7] K. K. Tripathi and M. A. Pavaskar, “Survey on Credit Card Fraud Detection Methods,” Int. J. Emerg. Technol. Adv. Eng., vol. 2, no. 11, p. 721, 2012.

      [8] T. P. Bhatla, V. Prabhu, and A. Dua, “Understanding Credit Card Frauds,” Cards Bus. Rev., vol. 1, no. 6, pp. 1–15, 2003.

      [9] A. Georgia, “Telecommunications fraud,” Ind. Eng. IE, vol. Jun2007, V, p. 2p; 2 Color Photographs, 2007.

      [10] D. Olszewski, “A probabilistic approach to fraud detection in telecommunications,” Knowledge-Based Syst., vol. 26, pp. 246–258, 2012. https://doi.org/10.1016/j.knosys.2011.08.018.

      [11] H. Sithic and T. Balasubramanian, “Survey of Insurance Fraud Detection Using Data Mining Techniques,” Int. J. Innov. Technol. Explor. Eng., vol. 2, no. 3, pp. 62–65, 2013.

      [12] S. S. Waghade, “A Comprehensive Study of Healthcare Fraud Detection based on Machine Learning,” vol. 13, no. 6, pp. 4175–4178, 2018.

      [13] S. Pandit, S. Wang, and C. Faloutsos, “NetProbe : A Fast and Scalable System for Fraud Detection in Online Auction Networks NetProbe : A Fast and Scalable System for Fraud Detection in Online Auction Networks,” Proc. 16th Int. Conf. World Wide Web, pp. 201–210, 2007. https://doi.org/10.1145/1242572.1242600.

      [14] J. K. Taitsman, “Educating Physicians to Prevent Fraud, Waste, and Abuse,” N. Engl. J. Med., vol. 364, no. 2, pp. 102–103, 2011. https://doi.org/10.1056/NEJMp1012609.

      [15] J. Sheehan and J. Goldner, “Beyond the Anti-Kickback Statute: New Entities, New Theories in Healthcare Fraud Prosecutions,” J. Health Law, vol. 242, no. c, pp. 1419–1420, 2007.

      [16] CMS, “DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Medicare & Medicaid Services,” no. October 2016.

      [17] A. Rashidian, H. Joudaki, and T. Vian, “No evidence of the effect of the interventions to combat health care fraud and abuse: A systematic review of literature,” PLoS ONE. 2012. https://doi.org/10.1371/journal.pone.0041988.

      [18] J. Carlson, “Painful side effects,” Mod. Healthc., 2013.

      [19] J. F. Dube, “Fraud in Health Care and Organized Crime,” Med. Health, vol. 94, no. 9, pp. 268–269, 2009.

      [20] G. A. Ogunbanjo and D. K. van Bogaert, “Ethics in health care: healthcare fraud,” South African Fam. Pract., vol. 56, no. 1, pp. 10–13, 2014.

      [21] L. Morris, “Perspective: Combating fraud in health care: An essential component of any cost containment strategy,” Health Aff., vol. 28, no. 5, pp. 1351–1356, 2009. https://doi.org/10.1377/hlthaff.28.5.1351.

      [22] C. L. and J. T. Mark Button, “Fraud typologies and victims of fraud,” pp. 1–40, 2010.

      [23] Y.-P. Huang, “Survey of Fraud Detection Techniques,” 2004, no. September, pp. 749–754.

      [24] J. Li, K.-Y. Huang, J. Jin, and J. Shi, “A survey on statistical methods for health care fraud detection,” Health Care Manag. Sci., vol. 11, no. 3, pp. 275–287, 2008. https://doi.org/10.1007/s10729-007-9045-4.

      [25] K. Yamanishi and G. Williams, “On-line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms,” pp. 320–324, 2000. https://doi.org/10.1145/347090.347160.

      [26] WIPRO, “Comparative Analysis of Machine Learning Techniques for Detecting Insurance Claims Fraud,” WIPRO Ltd., 2015.

      [27] A. M. Mubarek and E. Adali, “Multilayer perceptron neural network technique for fraud detection,” 2017 Int. Conf. Comput. Sci. Eng., pp. 383–387, 2017. https://doi.org/10.1109/UBMK.2017.8093417.

      [28] A. F. Ana-Ramona BOLOGA, Razvan BOLOGA, “Big Data and Specific Analysis Methods for Insurance Fraud Detection,” Database Syst. J., vol. 4, no. 4, pp. 30–39, 2013.

      [29] M. S. Anbarasi, “Fraud detection using outlier predictor in health insurance data,” no. Icices, 2017. https://doi.org/10.1109/ICICES.2017.8070750.

      [30] R. A. Bauder, T. M. Khoshgoftaar, A. Richter, and M. Herland, “Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims,” 2016. https://doi.org/10.1109/ICTAI.2016.0123.

      [31] N. Borse and N. Maitre, “HEALTH CARE INSURANCE FRAUD DETECTION: A DATA MINING PERSPECTIVE,” no. 2, pp. 52–56, 2015.

      [32] H. Brownell, R. Griffin, E. Winner, O. Friedman, and F. Happé, “Address for correspondence,” pp. 1–32, 2000.

      [33] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv., vol. 41, no. September, pp. 1–58, 2009. https://doi.org/10.1145/1541880.1541882.

      [34] S. Chen and A. Gangopadhyay, “A novel approach to uncover health care frauds through spectral analysis,” Proc. - 2013 IEEE Int. Conf. Healthc. Informatics, ICHI 2013, pp. 499–504, 2013. https://doi.org/10.1109/ICHI.2013.77.

      [35] K. S. & R. G. CLIFTON PHUA*, VINCENT LEE, “A comprehensive survey of data mining-based accounting-fraud detection research,” 2010 Int. Conf. Intell. Comput. Technol. Autom. ICICTA 2010, vol. 1, pp. 50–53, 2010.

      [36] D. Coderre, “Fraud Detection Using Digital Analysis,” EDP Audit. Control. Secur. Newsletter (EDPACS), vol. 27, no. 3, pp. 1–8, 1999. https://doi.org/10.1201/1079/43249.27.3.19990901/30268.1.

      [37] H. Cui, Q. Li, H. Li, and Z. Yan, “Healthcare Fraud Detection Based on Trustworthiness of Doctors,” 2016. https://doi.org/10.1109/TrustCom.2016.0048.

      [38] R. a Derrig, “Insurance fraud,” vol. 69, no. 3, pp. 271–287, 2002. https://doi.org/10.1111/1539-6975.00026.

      [39] C. Francis, N. Pepper, and H. Strong, “Using support vector machines to detect medical fraud and abuse,” 2011 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 8291–8294, 2011. https://doi.org/10.1109/IEMBS.2011.6092044.

      [40] A. Gangopadhyay and S. Chen, “Health Care Fraud Detection with Community Detection Algorithms,” 2016 IEEE Int. Conf. Smart Comput., pp. 1–5, 2016. https://doi.org/10.1109/SMARTCOMP.2016.7501694.

      [41] H. Peng and M. You, “The Health Care Fraud Detection Using the Pharmacopoeia Spectrum Tree and Neural Network,” pp. 2008–2013, 2016. https://doi.org/10.1109/TrustCom.2016.0306.

      [42] J. Jiwon Seo and O. Mendelevitch, “Identifying frauds and anomalies in Medicare-B dataset.,” Conf. Proc. ... Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2017, pp. 3664–3667, 2017.

      [43] M. Kirlidog and C. Asuk, “A Fraud Detection Approach with Data Mining in Health Insurance,” Procedia - Soc. Behav. Sci., vol. 62, pp. 989–994, 2012. https://doi.org/10.1016/j.sbspro.2012.09.168.

      [44] R. M. Konijn and W. Kowalczyk, “Finding fraud in health insurance data with two-layer outlier detection approach,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6862 LNCS, pp. 394–405, 2011. https://doi.org/10.1007/978-3-642-23544-3_30.

      [45] L. N. Lata, I. A. Koushika, and S. S. Hasan, “A Comprehensive Survey of Fraud Detection Techniques,” Int. J. Appl. Inf. Syst., vol. 10, no. 2, pp. 26–32, 2015. https://doi.org/10.5120/ijais2015451471.

      [46] K.-C. Lin and C.-L. Yeh, “Use of Data Mining Techniques to Detect Medical Fraud in Health Insurance,” Int. J. Eng. Technol. Innov., vol. 2, no. 2, pp. 126–137, 2012.

      [47] Q. Liu and M. Vasarhelyi, “Healthcare fraud detection: A survey and a clustering model incorporating Geo-location information,” 29th WORLD Contin. Audit. Report. Symp., 2013.

      [48] F. Lu and J. E. Boritz, “Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions,” pp. 633–640, 2005. https://doi.org/10.1007/11564096_63.

      [49] F. Lu, J. E. Boritz, and D. Covvey, “Adaptive Fraud Detection using Benford’s Law,” Adv. Artif. Intell. Proc., vol. 4013, pp. 347–358, 2006. https://doi.org/10.1007/11766247_30.

      [50] R. M. Musal, “Two models to investigate medicare fraud within unsupervised databases,” Expert Syst. Appl., vol. 37, no. 12, pp. 8628–8633, 2010. https://doi.org/10.1016/j.eswa.2010.06.095.

      [51] S. Sadiq, Y. Tao, Y. Yan, and M. L. Shyu, “Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method,” Proc. - 2017 IEEE 3rd Int. Conf. Multimed. Big Data, BigMM 2017, pp. 185–192, 2017. https://doi.org/10.1109/BigMM.2017.56.

      [52] A. Steventon, S. I. Chaudhry, Z. Lin, J. A. Mattera, and H. M. Krumholz, “Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomized trial of tele monitoring,” pp. 1–13, 2017. https://doi.org/10.1186/s12911-017-0426-4.

      [53] A. Tagaris, P. Mnimatidis, D. Koutsouris, and S. Member, “Implementation of a Prescription fraud detection software using RDBMS tools and ATC coding” no. November, pp. 5–7, 2009. https://doi.org/10.1109/ITAB.2009.5394458.

      [54] D. Thornton, G. Van Capelleveen, M. Poel, J. Van Hillegersberg, and R. M. Mueller, “Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data,” Proc. 16th Int. Conf. Enterp. Inf. Syst., pp. 684–694, 2014.

      [55] S. Viaene and G. Dedene, “Insurance fraud: issues and challenges,” Geneva Pap. Risk Insur. Pract., vol. 29, no. 2, pp. 313–333, 2004. https://doi.org/10.1111/j.1468-0440.2004.00290.x.

      [56] Y. Peng, G. Kou, A. Sabatka, Z. Chen, D. Khazanchi, and Y. Shi, “Application of Clustering Methods to Health Insurance Fraud Detection,” Serv. Syst. Serv. Manag. 2006 Int. Conf. IEEE, vol. 1, pp. 116–120, 2006. https://doi.org/10.1109/ICSSSM.2006.320598.

      [57] V. C. S. Lee, C. Phua, D. Alahakoon, and V. Lee, “Minority Report in Fraud Detection: Classification of Skewed Data Minority Report in Fraud Detection: Classification of Skewed Data,” no. January 2004.

      [58] M. Pejic-Bach, “Profiling intelligent systems applications in fraud detection and prevention: Survey of research articles,” ISMS 2010 - UKSim/AMSS 1st Int. Conf. Intell. Syst. Model. Simul., pp. 80–85, 2010. https://doi.org/10.1109/ISMS.2010.26.




Article ID: 15696
DOI: 10.14419/ijet.v7i4.15696

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.