Variable annuity portfolio valuation with SHapley Additive exPlanations

This paper proposes a new metamodeling framework for valuing variable annuity (VA) portfolios that utilizes SHapley Additive exPlanations (SHAP) to select a representative sample in an explainable manner. When applying metamodeling techniques to principle-based VA portfolio valuation, selecting a representative sample with clear and explainable criteria is necessary. The proposed approach involves (i) training a surrogate neural network with existing valuations for the same portfolio across multiple market scenarios and (ii) decomposing the overall risk of a VA contract into clearly separated contributions from each risk driver using SHapley Additive exPlanations. This decomposition results in an informative and explainable representation of the VA contract data, which can later be used to select the representative sample under a new market scenario. Furthermore, by fine-tuning the surrogate neural network with the selected representative sample, the proposed method offers a systematic way to improve the neural network’s performance in the new market scenario. Our numerical analyses show that the proposed method outperforms conventional methods in the existing literature in prediction accuracy and goodness of fit.