Insurance companies make extensive use of Monte Carlo simulations in their capital and solvency models. To overcome the computational problems associated with Monte Carlo simulations, most large life insurance companies use proxy models such as replicating portfolios (RPs). In this paper, we present an example based on a variable annuity guarantee, showing the main challenges faced by practitioners in the construction of RPs: the feature engineering step and subsequent basis function selection problem. We describe how neural networks can be used as a proxy model and how to apply risk-neutral pricing on a neural network to integrate such a model into a market risk framework. The proposed model naturally solves the feature engineering and feature selection problems of RPs.

中文翻译：

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人寿保险偿付能力计算的神经网络模型

保险公司在其资本和偿付能力模型中广泛使用蒙特卡罗模拟。为了克服与蒙特卡罗模拟相关的计算问题，大多数大型人寿保险公司使用代理模型，例如复制投资组合 (RP)。在本文中，我们提出了一个基于可变年金保证的示例，展示了从业者在构建 RP 时面临的主要挑战：特征工程步骤和随后的基函数选择问题。我们描述了如何将神经网络用作代理模型，以及如何在神经网络上应用风险中性定价以将这种模型集成到市场风险框架中。所提出的模型自然地解决了 RP 的特征工程和特征选择问题。