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Computational intelligence with applications to general insurance: a review

I – The role of statistical learning

Published online by Cambridge University Press:  18 May 2012

Pietro Parodi
Pietro Parodi*
Affiliation:
Willis Global Solutions (Consulting Group), London
*
*Correspondence to: Pietro Parodi, Willis Global Solutions (Consulting Group), Willis Ltd, 51 Lime Street, London EC3 M 7DQ. E-mail: pietro.parodi@willis.com
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Abstract

This paper argues that most of the problems that actuaries have to deal with in the context of non-life insurance can be usefully cast in the framework of computational intelligence (a.k.a. artificial intelligence), the discipline that studies the design of agents which exhibit intelligent behaviour. Finding an adequate framework for actuarial problems has more than a simply theoretical interest: it also allows a knowledge transfer from the computational intelligence discipline to general insurance, wherever techniques have been developed for problems which are common to both contexts. This has already happened in the past (neural networks, clustering, data mining have all found applications to general insurance) but not systematically, with the result that many useful computational intelligence techniques such as sparsity-based regularisation schemes (a technique for feature selection) are virtually unknown to actuaries.

In this first of two papers, we will explore the role of statistical learning in actuarial modelling. We will show that risk costing, which is at the core of pricing, reserving and capital modelling, can be described as a supervised learning problem. Many activities involved in exploratory analysis, such as data mining or feature construction, can be described as unsupervised learning. A comparison of different computational intelligence methods will be carried out, and practical insurance applications (rating factor selection, IBNER analysis) will also be presented.

Keywords

Statistical learningMachine learningIntelligent agentsRisk agentsRegularisationGLMNeural networks
Type
Papers
Information
Annals of Actuarial Science , Volume 6 , Issue 2 , 23 August 2012 , pp. 307 - 343
Copyright
Copyright © Institute and Faculty of Actuaries 2012

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References

Berry, J., Hemming, G., Matov, G., Morris, O. (2009). Report of the model validation and monitoring in personal lines pricing working party. GIRO Proceedings. Available at http://www.actuaries.org.uk/research-and-resources/documents/report-model-validation-and-monitoring-personal-lines-pricing-workiGoogle Scholar
Bishop, C.M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics) Springer.Google Scholar
Chhabra, A., Parodi, P. (2010). Dealing with sparse data: Practical Challenges and techniques. GIRO Proceedings. Available at http://www.actuaries.org.uk/research-and-resources/documents/b04-dealing-sparse-dataGoogle Scholar
Cummins, J.D., Derrig, R.A. (1997). Fuzzy financial pricing of property-liability insurance. North American Actuarial Journal, 1(4), 21–44.CrossRefGoogle Scholar
De Mol, C., De Vito, E., Rosasco, L. (2009). Elastic-net regularization in learning theory. Journal of Complexity, 25(2), 201–230.CrossRefGoogle Scholar
Dugas, C., Bengio, Y., Chapados, N., Vincent, P., Denoncourt, G., Fournier, C. (2003). Statistical learning algorithms applied to automobile insurance ratemaking. 2003 CAS Winter Forum, Casualty Actuarial Society.CrossRefGoogle Scholar
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32(2), 407–499.CrossRefGoogle Scholar
Grunwald, P.D., Myung, J., Pitt, M.A. (2005)? 2009 in text on page 8. Advances in minimum description length. MIT Press.CrossRefGoogle Scholar
Guo, L. (2003). Applying data mining techniques in property/casualty insurance. CAS Forum.Google Scholar
Hastie, T., Efron, B. (2012). lars: Least Angle Regression, Lass and Forward Stagewise. R package (http://cran.r-project.org/web/packages/lars/)Google Scholar
Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA.CrossRefGoogle Scholar
Hertz, J., Krogh, A., Palmer, R.G. (1991). Introduction to the theory of neural computation. Addison-Wesley Pub. Co., Redwood City, Calif.Google Scholar
Ioannidis, J.P.A. (2005). Why Most Published Research Findings Are False. PLoS Medicine (San Francisco: Public Library of Science) 2 (8).Google Scholar
Jacobsson, H. (2006). Rule Extraction from Recurrent Neural Networks. PhD dissertation. Department of Computer Science, University of Sheffield. Available at http://www.dcs.shef.ac.uk/intranet/research/phdtheses/Jacobsson2006.pdfGoogle Scholar
Lemaire, J. (1990). Fuzzy insurance. Astin Bulletin, 20(1), 33–55.CrossRefGoogle Scholar
Liang, T-P. (1992). A composite approach to inducing knowledge for expert systems design. Management Science, 38, No. 1, 1992.CrossRefGoogle Scholar
Lowe, J., Pryor, L. (1996). Neural networks v. GLMs in pricing general insurance. GIRO Proceedings. Available at http://www.actuaries.org.uk/research-and-resources/documents/neural-networks-vs-glms-pricing-general-insuranceGoogle Scholar
MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms (4th Printing). Cambridge University Press.Google Scholar
McCulloch, W.S., Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.CrossRefGoogle Scholar
Minsky, M.L., Papert, S.A. (1969). Perceptrons. Cambridge: MIT Press.Google Scholar
Modlin, C., Feldblum, S., Schirmacher, D., Schirmacher, E., Thandi, N., Anderson, D. (2004). A practitioner's guide to generalized linear models. CAS Discussion Paper.Google Scholar
Papadimitriou, C.M. (1994). Computational complexity. Addison-Wesley, Reading, Massachusetts.Google Scholar
Parodi, P. (2009). Computational intelligence techniques for general insurance. Research dissertation for the Actuarial Profession. Available at http://www.actuaries.org.uk/research-and-resources/documents/computational-intelligence-techniques-general-insuranceGoogle Scholar
Park, M.Y., Hastie, T. (2007). L1-regularization path algorithm for generalized linear models. Journal of the Royal Statistical Society. Series B (Methodological), 69(4), 659–677.CrossRefGoogle Scholar
Rasmussen, C.E., Williams, C.K. (2006). Gaussian Processes for Machine Learning. The MIT Press.Google Scholar
Rosasco, L., De Vito, E., Caponnetto, A., Piana, M., Verri, A. (2004). Are loss functions all the same? Neural Computation, 16(5), 1063–1076.CrossRefGoogle ScholarPubMed
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408.CrossRefGoogle ScholarPubMed
Rumelhart, D.E., Hinton, G.E., McClelland, J.L. (1987). A general framework for parallel distributed processing. In D.E. Rumelhart, J.L. McClelland, et al., editors, Parallel Distributed Processing: Volume 1: Foundations, pages 45–76. MIT Press, Cambridge.Google Scholar
Shapiro, A.F. (2004). Fuzzy logic in insurance. Insurance: Mathematics and Economics, 35(2), 399–424.Google Scholar
Taylor, G. (2008). A simple model of insurance market dynamics. GIRO proceedings. Available at http://www.actuaries.org.uk/research-and-resources/documents/simple-model-insurance-market-dynamics-slidesCrossRefGoogle Scholar
Terzopoulos, D., Tu, X., Grzeszczuk, R. (1994). Artificial fishes: autonomous locomotion, perception, behavior, and learning in a simulated physical world. Artif. Life,, 1(4), 327–351.CrossRefGoogle Scholar
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288.CrossRefGoogle Scholar
Tychonoff, A.N. (1943). “Об устойчивости обpaтньіхΙч зaдaч [On the stability of inverse problems]”. Doklady Akademii Nauk SSSR, 39(5), 195–198.Google Scholar
von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416.CrossRefGoogle Scholar
White, H. (2000). A Reality Check for Data Snooping. Econometrica.CrossRefGoogle Scholar
Yao, J. (2008). Clustering in ratemaking: with application in territories clustering. Casualty Actuarial Society Discussion Paper Program Casualty Actuarial Society - Arlington, Virginia, pages 170–192.Google Scholar
Zou, H., Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B, 67, 301–320.CrossRefGoogle Scholar