Holistic approach to fraud management in health insurance

Journal of Information and Organizational Sciences

Volumn 32, Issue 2, 2008, Pages 99-114

  Furlan, Štefan ^a   Bajec, Marko ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Activities; Characteristics; Fraud management system; Health care; Insurance

Indexed keywords

EID: 77956611311     PISSN: 18463312     EISSN: 18469418     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. AICPA. Statement on Auditing Standards No. 99: Consideration of Fraud in a Financial Statement Audit, 2002.
2. Artís, M; Ayuso, M; Guillén, M. Detection of Automobile Insurance Fraud with Discrete Choice Models and Misclassified Claims. The Journal of Risk and Insurance, Vol. 63, No. 3, pp. 325-340, 2002.
3. Babcock, C; McGee, M.K. Filter out the Frauds. InformationWeek, No. 995, pp. 45-49, 2004.
4. Bolton, R.J; Hand, D.J. Statistical Fraud Detection: A Review. Statistical Science, Vol. 17, pp. 235-249, 2002.
5. Brockett, P.L; Derrig, R.A; Golden, L.L; Levine, A; Alpert, M. Fraud Classification using Principal Component Analysis of RIDITs. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 341-371, 2002.
6. Cahill, M.H; Lambert, D; Pinheiro, J.C; Sun, D.X. Detecting Fraud in Real World. Handbook of Massive Data Sets, Kluwer Academic Publishers, pp. 913-930, 2002.
7. nd edition. John Willey and Sons Inc, 2007.
8. Chan, P.K; Fan, W; Prodromidis, A.L; Stolfo, S.J. Distributed Data Mining in Credit Card Fraud Detection. IEEE Intelligent Systems, pp. 67-74, 1999.
9. Counter Fraud and Security Management Service. Protecting our NHS, 2001.
10. Derrig, R.A. Insurance Fraud. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 271-287, 2002.
11. Dorronsoro, J; Ginel, F; Sanchez, C; Cruz, C. Neural Fraud Detection in Credit Card Operations. IEEE Transaction on Neural Networks, Vol. 8, No. 4, pp. 827-834, 1997.
12. Fawcett, T; Provost, F. Adaptive Fraud Detection. Data Mining and Knowledge Discovery, Kluwer, 1997.
13. Frieden, J. Health Care Fraud Detection Enters the Information Age. Business & Health, Vol. 10, No. 7, pp. 29-30, 1992.
14. He, H; Wang, J; Graco, W; Hawkins, S. Application of Neural Networks to Detection of Medical Fraud. Expert Systems with Applications, Vol. 13, No. 4, pp. 329-336, 1997.
15. Jou, S; Hebenton, B. Insurance fraud in Taiwan: Reflections on regulatory effort and criminological complexity. International Journal of the Sociology of Law, Vol. 35, pp. 127-142, 2007.
16. Kou, Y; Lu, C.T; Sirwongwattana, S; Huang, Y.P. Survey of fraud detection techniques. IEEE International Conference on Networking, Sensing and Control, 2004, Vol. 2, pp. 749-754, 2004.
17. Major, J; Riedinger, D.R. EFD: A Hybrid Knowledge/Statistical-Based System for the Detection of Fraud. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 309-324, 2002.
18. Phua, C; Lee, V; Smith, K; Gayler, R. A Comprehensive Survey of Data Miningbased Fraud Detection Research. Artificial Intelligence Review, 2005.
19. Rupnik, R; Kukar, M. Decision Support to Support Decision Processes with Data Mining. Journal of Information and Organizational Sciences, Vol. 31, No. 1, pp. 217-232, 2007.
20. Schiller, J. The Impact of Insurance Fraud Detection Systems.] The Journal of Risk and Insurance, Vol. 73, No. 3, pp. 421-438, 2006.
21. Selinšek, L. Kazenskopravni vidiki zavarovalniških goljufij - nekatera izhodiš a. Goljufije v zavarovalništvu, pp. 89-106, 2004.
22. Sokol, L; Garcia, B; West, M; Rodriguez, J; Johnson, K. Precursory Steps to Mining HCFA Health Care Claims. Proceedings of the 34th Hawaii International Conference on System Sciences, 2001.
23. Stempel, J.W. Recent Court Decisions. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 245-257, 2002.
24. Stolfo, S.J; Fan, W; Lee, W. Cost-based modeling for fraud and intrusion detection: results fromthe JAM project. DARPA Information Survivability Conference and Exposition, 2000. DISCEX '00. Proceedings, Vol. 2, pp. 130-144, 2000.
25. Tennyson, S; Salsas-Forn, P. Claims Auditing in Automobile Insurance: Fraud Detection and Deterrence Objectives. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 289-308, 2002.
26. Tuyls, K. Machine Learning Techniques for Fraud Detection. Master thesis, VUB, 2000.
27. Viaene, S; Derrig, R.A; Baesens, B; Dedene, G. A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection. The Journal of Risk and Insurance, Vol. 69, No. 3, pp. 373-421, 2002.
28. Viaene, S; Dedene, G; Derrig, R.A. Auto Claim Fraud Detection using Bayesian Learning Neural Networks. Expert Systems with Applications, Vol. 29, pp. 653-666, 2005.
29. Viaene, S; Ayuso, M; Guillen, M; Gheel, D.V; Dedene, G. Strategies for Detecting Fraudulent Claims in the Automobile Insurance Industry. European Journal of Operational Research, Vol. 176, No. 1, pp. 565-583, 2007.
30. Weisberg, H.I; Derrig, R.A. Fraud and Automobile Insurance: A Report on Bodily Injury Liability Claims in Massachusetts. Journal of Insurance Regulation, No. 9, pp. 497-541, 1991.
31. Weisberg, H.I; Derrig, R.A. Quantitative Methods for Detecting Fraudulent Automobile Bodily Injury Claims. Risques, Vol. 35, pp. 75-101, 1998.
32. Wheeler, R; Aitken, S. Multiple Algorithms for Fraud Detection. Knowledge- Based Systems, Vol. 13, pp. 93-99, 2000.
33. Xing D; Girolami, M. Employing Latent Dirichlet Allocation for Fraud Detection in Telecommunications. Pattern Recognition Letters, Vol. 28, No. 13, pp. 1818-1824, 2007.