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This article explores the application of machine learning models to predict mortality rates for pension fund participants, emphasizing the need for accurate mortality forecasting to assess financial soundness. The study compares various machine learning algorithms, including regression trees, random forests, and neural networks, against the traditional Lee-Carter model, finding that some machine learning methods offer competitive performance. The findings suggest that actuarial learning can enhance liabilit
- Author
- Manisha Lakhe
- Language
- EN