Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management

Abstract

This article explores the transformative potential of artificial intelligence (AI) in predicting life expectancy and its far-reaching implications for the annuities market. Traditional actuarial models, often reliant on demographic data and historical trends, face limitations in accuracy and personalization. We present a novel approach leveraging machine learning algorithms, including neural networks, decision trees, and ensemble methods, alongside natural language processing and deep learning techniques. Our AI-driven models integrate diverse data sources, including medical histories, genetic information, lifestyle factors, and socio-economic indicators, to provide more accurate and individualized life expectancy predictions. Using a dataset of anonymized health records and historical mortality data, we demonstrate that our AI models significantly outperform traditional actuarial tables in predicting individual mortality risks. The enhanced predictive power of these models has substantial implications for the annuities market, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital. Moreover, consumers benefit from fairer pricing and personalized product offerings. This article underscores the need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices. We also discuss ethical considerations, data privacy concerns, and the challenge of model interpretability, highlighting areas for future research to ensure responsible deployment of AI in life expectancy predictions and annuity pricing.