OPTIMIZING WITHDRAWAL RISK ASSESSMENT FOR GUARANTEED MINIMUM WITHDRAWAL BENEFITS IN INSURANCE USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Keywords:
Guaranteed Minimum Withdrawal Benefit (GMWB), Variable Annuities, Risk Assessmen, Market Volatility, Insurance ProductsAbstract
The valuation and pricing of GMWB which is a key value-added component of VA helping administrators control for longevity risk to retirees. GMWB provides systematic withdrawal options to policyholders and safeguard its clients against market volatility making it a significant product for retirement planning. In this research, provide a comprehensive overview of the structure of GMWB, outlining its cost expense and tax features and put forward a new analytic methodology to evaluate the risks of insurers that use such guarantees. The paper extends prior research by investigating current pricing frameworks of GMWB, while differentiating agents fixed and temporal withdrawal patterns. This paper discusses how occurrence factors such as market fluctuations, interest rates, and mortality assumptions influence GMWB pricing and risk. Unfortunately, the current pricing models have limitations, such as their inability to fully represent the characteristics of the financial markets and the stochastic behaviour of policyholders. It seems that most used models presuppose certain rather trivial conditions that hardly may be met in practice. Future research should focus on developing more sophisticated stochastic models, incorporating advanced risk factors, and improving the computational efficiency of pricing algorithms to better reflect the complexities of GMWB products and ensure more accurate risk management for insurers and retirees alike.
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