Profitability Impact of AI Adoption in Annuity Pricing

Abstract

The insurance enterprise faces growing challenges in optimizing annuity pricing strategies under changing market situations and growing opposition to its product portfolios. Conventional actuarial models have massive deficiencies, as they depend upon historical data patterns and linear assumptions that cannot capture complex, nonlinear relationships among risk factors and profitability results. The usage of machine learning algorithms in annuity pricing frameworks truly addresses core shortcomings in conventional methodologies, with particular regard to mortality risk assessment, sensitivity analysis of interest rates, and policyholder behavior prediction. Gradient boosting decision trees, neural network architectures, and ensemble methods applied to fixed and indexed annuity products achieve superior predictive accuracy compared to generalized linear models commonly found in traditional actuarial practice. This implementation framework integrates machine learning predictive capabilities with established actuarial practices to ensure regulatory compliance while maintaining mathematical soundness. Deep learning approaches to mortality forecasting, especially Long Short-Term Memory networks, transcend restrictions of classical Lee-Carter models by allowing temporal dependencies and nonlinear patterns to be captured, characteristic of modern mortality experiences. Reinforcement learning applications to derivative hedging strategies optimize dynamic rebalancing decisions for indexed annuity products with embedded options. Quantitative comparison among stochastic mortality models across heterogeneous populations reveals performance differences conditional upon demographic characteristics and projection horizons. Spread margin enhancement through predictive analytics allows a more sophisticated crediting rate determination and policyholder retention strategy. ROI considerations include not only direct improvements in profitability but also indirect operational efficiency gains, balanced against infrastructure and personnel investment requirements.