Neural networks meet least squares Monte Carlo at internal model data - Christian Jonen; Tamino Meyhöfer; Zoran Nikolić (European Actuarial Journal, 2022)
In August 2020 we published "Comprehensive Internal Model Data for Three Portfolios" as an outcome of our work for the committee "Actuarial Data Science" of the German Actuarial Association. The data sets include realistic cash-flow models outputs used for proxy modelling of life and health insurers. Using these data, we implement the hitherto most promising model in proxy modeling consisting of ensembles of feed-forward neural networks and compare the results with the least squares Monte Carlo (LSMC) polynomial regression. To date, the latter represents—to our best knowledge—the most accurate proxy function productively in use by insurance companies. An additional goal of this publication is a more precise description of "Comprehensive Internal Model Data for Three Portfolios" for other researchers, practitioners and regulators interested in developing solvency capital requirement (SCR) proxy models.
Keywords: 510; Least squares Monte Carlo; Neural networks; Solvency II; Proxy modeling; Insurance risk management; Cash flow projection models; Risk-neutral valuation; Machine learning