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4. P-Thinned Gamma Process and Corresponding Random Walk

Author

Jordanova, Pavlina, Stehlík, Milan, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Dimov, Ivan, editor, Faragó, István, editor, and Vulkov, Lubin, editor

Published
2019
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6. Machine Learning in Insurance.

Author

Nielsen, Jens Perch, Asimit, Alexandru, Kyriakou, Ioannis, and Nielsen, Jens Perch

Subjects
History of engineering & technology, Bornhuetter-Ferguson, GLM, Solvency II, VaR estimation, accelerated failure time model, analyzing financial data, autocorrelation, automobile insurance, benchmark, calibration, canonical parameters, chain ladder, chain-ladder method, claims prediction, cross-validation, deposit insurance, dichotomous response, exponential families, export credit insurance, generalised linear modelling, implied volatility, least-squares monte carlo method, life insurance, local linear kernel estimation, long-term forecasts, machine learning, maximum likelihood, n/a, non-life reserving, operational time, overdispersion, overlapping returns, parameterization, prediction, predictive model, prior knowledge, proxy modeling, risk classification, risk selection, semiparametric modeling, static arbitrage, stock return volatility, telematics, tree boosting, validation, zero-inflated poisson model, zero-inflation
Abstract

Summary: Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries' "preferred methods" were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

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