Hybrid Information Systems pp 695-710 | Cite as
Insurance Applications of Soft Computing Technologies
Conference paper
Abstract
The purposes of the article are twofold: first, to review soft computing (SC) applications in insurance so as to document the unique characteristics of insurance as an application area; and second, to document the extent to which hybrid SC technologies have been employed. While it is clear that SC has made inroads into many facets of the business, in most instances the applications did not capitalized on the synergies between the SC technologies and, as a consequence, there are opportunities to extend the studies.
Keywords
soft computing insurance applications neural networks fuzzy logic genetic algorithmsPreview
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References
- Abraham A, Nath B (2000) Hybrid intelligent systems design-A review of a decade of research. Technical Report (5/2000), Gippsland School of Computing and Information Technology, Monash University, Australia, 54 pagesGoogle Scholar
- Bakheet MT (1995) Contractors’ Risk Assessment System (Surety, Insurance, Bonds, Construction, Underwriting). Ph.D. Dissertation, Georgia Institute of TechnologyGoogle Scholar
- Bellman R, Zadeh LA (1970) Decision-Making in a Fuzzy Environment. Management Science 17: 141–164MathSciNetCrossRefGoogle Scholar
- Boissonnade AC (1984) Earthquake Damage and Insurance Risk. Ph.D. Dissertation, Stanford UniversityGoogle Scholar
- Bonissone PP (1998) Soft Computing Applications: the Advent of Hybrid Systems. In: Bosacchi B, Fogel DB, Bezdek JC (eds) Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation. Proceedings of SPIE 3455: 63–78.Google Scholar
- Brockett PL, Cooper WW, Golden LL, Pitaktong U (1994) A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency. J Risk and Insurance 61 (3): 402–424CrossRefGoogle Scholar
- Brockett PL, Xia X, Derrig RA (1998) Using Kohonen’s Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud. J Risk and Insurance 65 (2): 245–274CrossRefGoogle Scholar
- Buckley JJ, Hayashi Y (1994) Fuzzy Neural networks. In: Yager RR, Zadeh LA (eds) Fuzzy Sets, Neural Networks, and Soft Computing. Van Nostrand Reinhold, New York, pp 233–249.Google Scholar
- Chang C, Wang P (1995) The Matching of Assets and Liabilities with Fuzzy Mathematics. 25th International Congress of Actuaries, 123–137Google Scholar
- Chorafas DN (1994) Chaos Theory In The Financial Markets: Applying Fractals, Fuzzy Logic, Genetic Algorithms Probus Publishing Company, ChicagoGoogle Scholar
- Cummins JD, Derrig RA (1993) Fuzzy Trends in Property-Liability Insurance Claim Costs. J Risk and Insurance 60 (3): 429–465CrossRefGoogle Scholar
- Cummins JD, Derrig RA (1997) Fuzzy Financial Pricing of Property-Liability Insurance. North American Actuarial Journal 1 (4): 21–44MathSciNetMATHCrossRefGoogle Scholar
- Derrig RA, Ostaszewski K (1995) Fuzzy Techniques of Pattern Recognition in Risk and Claim Classification. J Risk and Insurance 62 (3): 447–482CrossRefGoogle Scholar
- Derrig RA, Ostaszewski K (1997) Managing the Tax Liability of a Property-Liability Insurance Company. J Risk and Insurance 64 (4): 595–711MathSciNetGoogle Scholar
- DeWit GW (1982) Underwriting and Uncertainty. Insurance: Mathematics and Economics 1: 277–285MathSciNetCrossRefGoogle Scholar
- Erbach DW, Seah E (1993) Discussion of: Young VR The Application of Fuzzy Sets to Group Health Underwriting. Transactions of the Society of Actuaries 45: 585–587Google Scholar
- Horgby P, Lohse R, Sittaro N (1997) Fuzzy Underwriting: An Application of Fuzzy Logic to Medical Underwriting. J Actuarial Practice 5 (1): 79–104MATHGoogle Scholar
- Huang C, Dorsey RE, Boose MA (1994) Life Insurer Financial Distress Prediction: A Neural Network Model. J Insurance Regulation Winter 13 (2): 131–167Google Scholar
- Ismael MB (1999) Prediction of Mortality and In-hospital Complications for Acute Myocardial Infarction Patients Using Artificial Neural Networks. Ph.D. dissertation, Duke UniversityGoogle Scholar
- Jain LC, Martin NM (eds) (1999) Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press, New YorkGoogle Scholar
- Jablonowski M (1997) Modeling Imperfect Knowledge in Risk Management and Insurance. Risk Management and Insurance Review 1 (1): 98–105CrossRefGoogle Scholar
- Jang J (1997) Comparative Analysis of Statistical Methods and Neural Networks for Predicting Life Insurers’ Insolvency (Bankruptcy). Ph.D. dissertation, University of Texas at AustinGoogle Scholar
- Jang, J-SR (1993) ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23 (3): 665–685CrossRefGoogle Scholar
- Karr CL (1991) Genetic algorithms for fuzzy controllers. AI Expert 6 (2): 27–33Google Scholar
- Kieselbach R (1997) Systematic Failure Analysis Using Fault Tree and Fuzzy Logic. Technology, Law and Insurance 2: 13–20CrossRefGoogle Scholar
- Lee B, Kim M (1999) Application of Genetic Algorithm to Automobile Insurance for Selection of Classification Variables: The Case of Korea. Paper presented at the 1999 Annual Meeting of the American Risk and Insurance AssociationGoogle Scholar
- Lee MA, Tagaki H (1993) Dynamic control of genetic algorithm using fuzzy logic techniques. In: Forrest S (ed) Proceedings of the fifth International Conference on Genetic Algorithms Morgan Kaufmann, pages 76–83Google Scholar
- Lemaire J (1990) Fuzzy Insurance. ASTIN Bulletin 20 (1): 33–55CrossRefGoogle Scholar
- Liska J, Melsheimer SS (1994) Complete design of fuzzy logic systems using genetic algorithms Proceedings of 3rd IEEE international conference on fuzzy systems, pp 1377–1382.Google Scholar
- Lokmic L, Smith KA (2000) Cash flow forecasting using supervised and unsupervised neural networks. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks 6: 343–347Google Scholar
- Ostaszewski K (1993) Fuzzy Set Methods in Actuarial Science. Society of Actuaries, Schaumburg, ILGoogle Scholar
- Park J (1993) Bankruptcy Prediction of Banks and Insurance Companies: an Approach Using Inductive Methods. Ph.D. dissertation, University of Texas at Austin Refenes AP (1995) Neural Networks in the Capital Markets. John Wiley & Sons, New YorkGoogle Scholar
- Refenes AP, Abu-Mostafa YP (eds) (1996) Neural Networks in Financial Engineering: Proceedings of the Third International Conference on Neural Networks in the Capital Markets. World Scientific Pub Co, LondonGoogle Scholar
- Saemundsson SR (1996) Dental Caries Prediction by Clinicians and Neural Networks. Ph.D. dissertation, University of North Carolina at Chapel HillGoogle Scholar
- Siegel PH, de Korvin A, Omer K (eds) (1995) Applications of Fuzzy Sets and the Theory of Evidence to Accounting. JAI Press, Greenwich, ConnGoogle Scholar
- Tan R (1997) Seeking the Profitability-Risk-Competitiveness Frontier Using a Genetic Algorithm. J Actuarial Practice 5 (1): 49–77MATHGoogle Scholar
- Tu JV (1993) A Comparison of Neural Network and Logistic Regression Models for Predicting Length of Stay in the Intensive Care Unit Following Cardiac Surgery. Ph.D. dissertation, University of TorontoGoogle Scholar
- Vaughn ML (1996) Interpretation and Knowledge Discovery from a Multilayer Perceptron Network: Opening the Black Box. Neural Computing and Applications 4 (2): 72–82CrossRefGoogle Scholar
- Vaughn ML, Ong E, Cavill SJ (1997) Interpretation and Knowledge Discovery from a Multilayer Perceptron Network that Performs Whole Life Assurance Risk Assessment. Neural Computing and Applications 6: 201–213CrossRefGoogle Scholar
- Wendt RQ (1995) Build Your own GA Efficient Frontier. Risks and Rewards, December: 1, 4–5Google Scholar
- Young VR (1993) The Application of Fuzzy Sets to Group Health Underwriting. Transactions of the Society of Actuaries 45: 551–590Google Scholar
- Young VR (1996) Insurance Rate Changing: A Fuzzy Logic Approach. J Risk and Insurance 63: 461–483CrossRefGoogle Scholar
- Young VR (1997) Adjusting Indicated Insurance Rates: Fuzzy Rules that Consider Both Experience and Auxiliary Data. Proceedings of the Casualty Actuarial Society 84: 734–765Google Scholar
- Zadeh LA (1994) The Role of Fuzzy Logic in Modeling, Identification and Control. Modeling Identification and Control 15 (3), 191MathSciNetMATHCrossRefGoogle Scholar
- Zhao J (1996) Maritime Collision and Liability. Ph.D. Dissertation, University of SouthamptonGoogle Scholar
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© Springer-Verlag Berlin Heidelberg 2002