Artificial Intelligence for Mortality Risk Prediction in Life Insurance: Advanced Techniques and Model Validation

Abstract

Accurately assessing mortality risk plays a critical role in the life insurance industry, impacting premium pricing, product development, and financial solvency. Traditional actuarial methods, while effective, often rely on historical data and pre-defined risk factors, potentially overlooking complex relationships and emerging risk trends. This research paper explores the burgeoning application of Artificial Intelligence (AI) in mortality risk prediction, focusing on advanced techniques and the crucial aspects of model validation for real-world implementation.

The paper delves into the potential of deep learning architectures like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for capturing non-linear relationships and identifying hidden patterns in vast datasets encompassing traditional actuarial variables, medical records, socio-economic indicators, and potentially, behavioral data. We explore the advantages of these techniques in uncovering previously unknown risk factors and improving prediction accuracy compared to traditional models. For instance, RNNs can effectively model sequential data such as medical history or electronic health records, capturing the temporal evolution of health status and its impact on mortality risk. Similarly, CNNs can process complex data structures like medical images, extracting subtle features that may be undetectable by traditional methods and contributing to a more comprehensive risk assessment.

However, the growing adoption of AI in insurance raises concerns regarding the "black-box" nature of certain algorithms, where interpretability and justification for their predictions remain opaque. To address this challenge, the paper examines Explainable AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). These approaches provide insights into the internal workings of AI models, allowing actuaries to understand how specific variables contribute to risk assessments and fostering trust in the decision-making process. For instance, LIME can explain individual predictions by generating a simplified local model around a specific data point, highlighting the most influential factors for that particular case. SHAP values, on the other hand, can be used to explain the overall contribution of each variable to the model's predictions, providing a more holistic understanding of how the model arrives at its conclusions.

Furthermore, the paper emphasizes the crucial role of model validation in ensuring the robustness and generalizability of AI models in life insurance. We explore rigorous validation techniques including k-fold cross-validation, calibration plots, and backtesting to assess the model's performance on unseen data and mitigate the risk of overfitting. Additionally, the paper addresses potential biases that may be inadvertently introduced during model development due to data imbalances or historical underwriting practices. Techniques for bias mitigation such as data augmentation and fairness-aware training algorithms are discussed.

Finally, the paper considers the practical implications of deploying AI models for mortality risk prediction in the life insurance industry. The paper explores integration strategies with existing actuarial frameworks, regulatory considerations, and potential ethical concerns surrounding data privacy and discrimination. By addressing these challenges, we can navigate the responsible adoption of AI for a more data-driven and risk-adjusted life insurance landscape.

This research paper contributes to the growing body of knowledge surrounding AI-powered mortality risk prediction in life insurance. By exploring advanced AI techniques, emphasizing robust model validation practices, and addressing ethical concerns, the paper aims to inform future research and guide the responsible implementation of AI in the insurance sector.