Risk evaluation in the insurance company using REFII model

Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods

Volumn , Issue , 2012, Pages 84-104

  Klepac, Goran ^a  

^a Strategic and Reporting Department   (Croatia)

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EID: 84898306643     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-4666-2542-6.ch005     Document Type: Chapter

References (24)

1. Aleksander, I., & Morton, H. (1995). An introduction to neural computing. New York, NY: International Thompson Computer Press.
2. Apte, C., Grossman, E., Pednault, E., Rosen, B., Tipu, F., & White, B. (1999). Insurance risk modeling using data mining technology. Report RC-21314. Yorktown Heights, NY: IBM Research Division.
3. Berry, J. A. M., & Linoff, G. (1997). Data mining techniques for marketing sales and customer support. New York, NY: John Wiley & Sons Inc.
4. Chidanand, A., Edna, G., Edwin, P. D., Pednault, B. K. R., Fateh, A. T., & Brian, W. (1999). Probabilistic estimation-based data mining for discovering insurance risks. IEEE Intelligent Systems, 14(6), 49-58. doi:10.1109/5254.809568
5. Derrig, R. A., & Ostaszewski, K. M. (1995). Fuzzy techniques of pattern recognition in risk and claim classification. The Journal of Risk and Insurance, 62(3), 447-482. doi:10.2307/253819
6. Hackerman, D. (1995). A tutorial on learning with bayesian networks microsoft research. MSR-TR-95-06. Seattle, WA: Microsoft.
7. Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques. San Francisco, CA: Morgan Kaufmann.
8. Kantardžić, M. (2003). Data mining: Concepts, models, methods and algorithms. New York, NY: John Wiley & Sons. doi:10.1115/1.2123107
9. Klepac, G. (2005). Time series rule discovering with unique model of transformation. (Unpublished Doctoral Dissertation). University of Zagreb FOI. Varaždin, Croatia.
10. Klepac, G., & Mršić, L. (2006). Poslovna inteligencija kroz poslovne slučajeve. Zagreb, Croatia: Liderpress.
11. Kohonen, T. (2001). Self-organizing maps. Berlin, Germany: Springer. doi:10.1007/978-3-642-56927-2
12. Kolyshkina, I. (2002). Modeling insurance risk: A comparison of data mining and logistic regression approaches. London, UK: PricewaterhouseCoopers.
13. Pratt, K. (2001). Locating patterns in discrete time series. (M.Sc. Thesis). University of South Florida. Tampa, FL.
14. Pyle, D. (1999). Data preparation for data mining. San Francisco, CA: MKP.
15. Samson, D. (1986). Designing an automobile insurance classification system. European Journal of Operational Research, 27, 235-241. doi:10.1016/0377-2217(86)90065-2
16. Samson, D., & Thomas, H. (1987). Linear models as aids in insurance decision making: The estimation of automobile insurance claims. Journal of Business Research, 15, 247-256. doi:10.1016/0148-2963(87)90027-0
17. Smith, K. A., Willis, R. J., & Brooks, M. (2000). An analysis of customer retention and insurance claim patterns using data mining: A case study. The Journal of the Operational Research Society, 51(5), 532-541.
18. Thomas, L., Edelman, D., & Crook, J. (2002). Credit scoring and its application. New York, NY: SIAM. doi:10.1137/1.9780898718317
19. Tryfos, P. (1980). On classification in automobile insurance. The Journal of Risk and Insurance, 47(2), 331-337. doi:10.2307/252336
20. Vaughan, E. J., & Vaughan, T. M. (1996). Fundamentals of risk and insurance (7th ed.). New York, NY: Wiley.
21. Vose, D. (2000). Quantitative risk analysis. New York, NY: John Wiley & Sons.
22. Williams, J. G., Weiqian, L., & Mehmet, O. (2001). Temporal data mining using hidden Markov-local polynomial models. Lecture Notes in Artificial Intelligence, 2035.
23. Williams, J. G., Weiqiang, L., & Mehmet, A. O. (2002). An overview of temporal data mining. In Proceedings of the 1st Australian Data Mining Workshop (ADM 2002), (pp. 83-90). Canberra, Australia: University of Technology.
24. Xsniaping, G. (1998). Pattern matching in financial time series data. Retrieved from http://www.datalab.uci.edu/people/xge/chart