evaluation of a variable annuity portfolio with death benefit using machine learning methods

Equity-linked insurance, also known as variable annuities (va), are modern life insurance contracts geared with investment vehicles. although these contractsare not fully introduced in the insurance market of iran, according to their significantpopularity among developed countries, it is expected that these contracts may have aconsiderable market share in the future. due to the nature of these contracts, the areaof research on vas is an interdisciplinary area of actuarial science and mathematicalfinance, while the most important objective is to evaluate options included in vas. inthis research, our main purpose is to study and elaborate various applications of dataclustering and machine learning on the estimation of the risk charge of a large portfolioof vas with guaranteed minimum death benefits. the results of this research indicatethat by applying machine learning algorithms, we reach the estimated risk charge of theportfolio in considerably shorter time while the difference between estimated value andcalculated value by monte carlo simulation is not significant. this will enable us tomodify its application for the iranian insurance industry.