Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies

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ID: 117732
2020
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Abstract
Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests.
Reference Key
krah2020risksmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Anne-Sophie Krah;Zoran Nikolić;Ralf Korn;Krah, Anne-Sophie;Nikolić, Zoran;Korn, Ralf;
Journal risks
Year 2020
DOI
10.3390/risks8010021
URL
https://doi.org/10.3390/risks8010021
Keywords
Machine learning life insurance solvency ii proxy modeling least-squares monte carlo method

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