Research from Kaiserslautern University of Technology Provides New Study Findings on Machine Learning (Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies)

2020 MAR 12 (NewsRx) -- By a News Reporter-Staff News Editor at Engineering Business Daily -- New study results on machine learning have been published. According to news reporting out of Kaiserslautern, Germany, by NewsRx editors, research stated, “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.”

The news correspondents obtained a quote from the research from Kaiserslautern University of Technology: “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.”

According to the news reporters, the research concluded: “Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests.”

For more information on this research see: Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies. Risks, 2020,8(1):21. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks8010021.

Our news editors report that more information may be obtained by contacting Anne-Sophie Krah, Department of Mathematics, Kaiserslautern University of Technology, Erwin-Schrodinger-Strasse, Geb. 48, 67653 Kaiserslautern, Germany. Additional authors for this research include Zoran Nikolic, Ralf Korn.

(Our reports deliver fact-based news of research and discoveries from around the world.)