In practice, the valuation of a large volume variable annuity contracts relies on Monte Carlo simulation which is quite computationally intensive. To build a more efficient valuation process, statistical models have been used within a data mining framework that consists of two subsequent stages: the data sampling stage to create a set of representative contracts, and the regression modeling stage to make predictions for the remaining contracts in the portfolio. In this article, we work with a new data mining framework based on active learning, in which we iteratively update the regression model efficiently by selecting the most informative representatives. Our metrics take into consideration both the ambiguity and the diversity of the prediction, which allow us to propose two methods that fit well in this active learning framework. Experimental results demonstrate the effectiveness of the proposed active learning approaches over the random sampling as well as the two-stage data mining framework.

在实践中，大额可变年金合同的估值依赖于计算量很大的蒙特卡洛模拟。为了建立更有效的评估流程，在数据挖掘框架中使用了统计模型，该框架包括两个后续阶段：数据采样阶段（用于创建一组代表性合同）和回归建模阶段（用于对剩余合同进行预测）投资组合。在本文中，我们使用基于主动学习的新数据挖掘框架，在该框架中，我们通过选择信息最丰富的代表来有效地迭代更新回归模型。我们的指标同时考虑了预测的不确定性和多样性，这使我们能够提出两种非常适合此主动学习框架的方法。