用机器学习预测养老金参保人死亡率，提升长期风险评估准确性
Actuarial Learning for Pension Fund Mortality Forecasting
- 将随机森林、XGBoost等机器学习模型用于养老金死亡率预测
- 部分模型在样本外表现优于经典Lee-Carter模型
- 适合精算师与养老金风险管理从业者参考
为评估养老金基金的财务稳健性，需将死亡率预测纳入未来现金流分析以持续管理长寿风险。本文将机器学习与人工智能技术应用于精算科学（即‘精算学习’），对一组养老金参保人进行死亡率预测。该领域涵盖回归树、随机森林、提升方法、XGBoost、CatBoost及神经网络（如FNN、LSTM、MHA）等算法与计算模型的应用。结果表明，若干机器学习/人工智能算法在样本外预测性能上可媲美甚至超越经典的Lee-Carter模型，为负债一致性评估与有效的养老金风险管理提供了新路径。
原文摘要 · Abstract (English)
For the assessment of the financial soundness of a pension fund, it is necessary to take into account mortality forecasting so that longevity risk is consistently incorporated into future cash flows. In this article, we employ machine learning models applied to actuarial science ({\it actuarial learning}) to make mortality predictions for a relevant sample of pension funds' participants. Actuarial learning represents an emerging field that involves the application of machine learning (ML) and artificial intelligence (AI) techniques in actuarial science. This encompasses the use of algorithms and computational models to analyze large sets of actuarial data, such as regression trees, random forest, boosting, XGBoost, CatBoost, and neural networks (eg. FNN, LSTM, and MHA). Our results indicate that some ML/AI algorithms present competitive out-of-sample performance when compared to the classical Lee-Carter model. This may indicate interesting alternatives for consistent liability evaluation and effective pension fund risk management.
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