Predictive Analytics and Machine Learning – Practical Applications for Actuarial Modeling (Nested Stochastic)
May 2023
Authors
Jean-Philippe Larochelle, FSA, MAAA, CERA
Peter Carlson, FSA, MAAA
Vincent Carrier Cote, FSA, MAAA
Ying Lu, FSA, MAAA
Noah Shapiro, FSA
Alex Tam, ASA, MAAA
Viresh Thusu
Ally Zhang, FSA, MAAA
The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.
Materials
Predictive Analytics and Machine Learning - Simplified Chinese
Podcast
Acknowledgments
The researchers’ deepest gratitude goes to those without whose efforts this project could not have come to fruition: the Project Oversight Group for their diligent work overseeing, reviewing, and editing this report for accuracy and relevance.
Project Oversight Group members:
Carlos Brioso, FSA, CERA
Alex Hookway, FSA, MAAA, CERA
Karen Jiang, FSA, CERA
Hezhong (Mark) Ma, FSA, MAAA
Sambhaji Shedale, FSA
Andy Smith, FSA, MAAA
Feng Sun, FSA, CERA
Andrei Titioura, FSA, FCIA, MAAA
At the Society of Actuaries Research Institute:
Korrel Crawford, Senior Research Administrator
David Schraub, FSA, MAAA, CERA, AQ, Senior Practice Research Actuary
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