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A variable annuity is a modern life insurance product that offers its policyholders participation in investment with various guarantees. To address the computational challenge of valuing large portfolios of variable annuity contracts, several data mining frameworks based on statistical learning have been proposed in the past decade. Existing methods utilize regression modeling to predict the market value of most contracts. Despite the efficiency of those methods, a regression model fitted to a small amount of data produces substantial prediction errors, and thus, it is challenging to rely on existing frameworks when highly accurate valuation results are desired or required. In this paper, we propose a novel hybrid framework that effectively chooses and assesses easy-to-predict contracts using the random forest model while leaving hard-to-predict contracts for the Monte Carlo simulation. The effectiveness of the hybrid approach is illustrated with an experimental study.
To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.
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