Predictive Analytics in Life and Annuity Reinsurance

June 2025

Author(s)

Sandeep Patil, FSA, CERA, MAAA

Michael Descy, CIA, CISA, MCM

Tricia Matson, FSA, MAAA

Executive Summary

This report consolidates the findings from literature research and informal interviews with subject matter experts (SMEs) on the adoption and challenges of predictive analytics in the insurance industry, focusing particularly on life and annuity reinsurance. The research team conducted a comprehensive investigation into various areas of the insurance industry to understand the current landscape and the role of predictive analytics.

The adoption of predictive analytics in the reinsurance industry has been slow but is gradually growing, with reinsurers focusing on areas such as risk selection, underwriting efficiency, and portfolio optimization. Despite the continued reliance on manual processes, the integration of predictive analytics is transforming traditional practices, creating new opportunities for growth and efficiency. These advanced tools have the potential to enhance risk assessment, streamline operations, and improve customer experience. However, challenges such as fragmented data architecture, outdated technologies, a shortage of skilled professionals, and regulatory compliance must be addressed to fully realize their benefits.

The life, annuity, and property and casualty (P&C) insurance industries are increasingly adopting predictive analytics to streamline claims processing, enhance underwriting accuracy, and improve customer engagement. Techniques such as supervised and unsupervised learning, natural language processing, and computer vision, which are utilized in these sectors, can also be applied in reinsurance to improve risk assessment, operational efficiency, and decision-making processes.

Regulatory developments in various regions stress the importance of transparency, fairness, and accountability in predictive analytics applications, highlighting the necessity for compliance and ethical considerations. By adopting responsible artificial intelligence (AI) and ensuring explainability, advancements can be aligned with industry ethical standards. This approach would enable reinsurers to utilize predictive analytics for strategic decision-making and provide superior value to their clients.

Material

Predictive Analytics in Life and Annuity Reinsurance

Acknowledgements

The researchers’ deepest gratitude goes to those without whose efforts this project could not have come to fruition: the Project Oversight Group and others for their diligent work overseeing questionnaire development, analyzing, and discussing respondent answers, and reviewing and editing this report for accuracy and relevance.

Project Oversight Group members:

Gershon Firestone, FSA, MAAA

Feng Sun, FSA, CERA

Bill Mehilos, FSA, MAAA

Guojun Cao, FSA, MAAA

Min Ji, FSA, FIA, MAAA

Nihar Malali

Shisheng Qian, FSA, CERA

At the Society of Actuaries Research Institute:

Kara Clark, FSA, MAAA, Senior Research Actuary

Barbara Scott, Senior Research Administrator

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