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.

为了评估大量的可变年金（VA）合同组合，许多保险公司都依赖于计算量大的蒙特卡洛模拟。为了解决这一计算难题，近年来采用了机器学习技术来估计大量合同的公平市场价值（FMV）。结果表明，自举聚合（装袋）是最流行的机器学习算法之一，在使用相关属性评估VA合同方面表现良好。在本文中，我们强调了套袋的预测偏差的存在，并使用偏差校正（BC）的套袋方法来减少偏差，从而提高预测性能。实验结果证明了与装袋，增压和模型点相比，BC装袋在预测准确性方面的有效性。