AI / Data Science
In Pursuit of Responsible AI – An Actuarial Profession Take
In 2024, the International Actuarial Association established a dedicated Task Force to guide the actuarial profession's response to Artificial Intelligence. After two years of work spanning governance, education, professionalism, and the evolving role of the actuary, the Task Force is proud to present three foundational papers on the responsible use of AI in this webinar.
Speaker: Bernice Lim, Chadwick Cheung, Dorothy Andrews
Moderator: Peter Withey
Speaker: Bernice Lim, Chadwick Cheung, Dorothy Andrews
Moderator: Peter Withey
Members Only
AI / Data Science
PBSS: AI for Pension Funds: Insights and Use Cases
Unlock how Artificial Intelligence is reshaping pensions and social security work. Join us for an insightful session featuring leading actuarial AI expert Ronald Richman, who will share practical applications of AI in longevity modelling, experience analysis, and actuarial workflow automation. This webinar will highlight real-world use insights, emerging opportunities, and what actuaries doing work in pensions and social security need to know to stay ahead in an AI-driven future.
Be sure to join us for what promises to be an informative and engaging discussion.
Speaker: Ronald Richman
Moderator: John Anderson
Be sure to join us for what promises to be an informative and engaging discussion.
Speaker: Ronald Richman
Moderator: John Anderson
Members Only
AI / Data Science
ASTIN: Explainable AI for Claims Reserving: Bridging Actuarial Practice and Machine Learning
This session brings together actuarial and machine learning perspectives to explore how AI can support reserving practices.
The actuarial perspective will cover the practical limitations of current reserving workflows, where judgment enters traditional methods and why consistency is difficult, and what actuaries need from AI, including transparency, validation, and governance. It will also include a case study comparing traditional results with AI-supported modeling and how ML results can serve as a second-opinion framework.
The machine learning perspective will cover the Bayesian ML framework, including its architecture, model selection based on predictive power and cross-validation, and how explainability is built into the model. It will also discuss why full distributions matter more than point estimates and share technical lessons from applying ML to insurance triangles.
Speakers: Ben Zickel and Yulia Nechay Moderator: Joana Raposo
The actuarial perspective will cover the practical limitations of current reserving workflows, where judgment enters traditional methods and why consistency is difficult, and what actuaries need from AI, including transparency, validation, and governance. It will also include a case study comparing traditional results with AI-supported modeling and how ML results can serve as a second-opinion framework.
The machine learning perspective will cover the Bayesian ML framework, including its architecture, model selection based on predictive power and cross-validation, and how explainability is built into the model. It will also discuss why full distributions matter more than point estimates and share technical lessons from applying ML to insurance triangles.
Speakers: Ben Zickel and Yulia Nechay Moderator: Joana Raposo
Members Only
AI / Data Science
Earth, Wind and Fire – Finding and Using Climate Data
Climate data is everywhere. Accessible climate data for actuarial modeling, not so much.
Building on the American Academy of Actuaries’ paper "Climate Data: Actuarial Perspectives on Quality, Challenges, and Effective Risk Quantification", this webinar explores the current landscape of climate data through the lens of various actuarial practice areas. The presentation highlights significant issues regarding data quality and availability, specifically addressing blind spots such as the reporting lag bias in Property & Casualty loss data and the complexity of attributing Life and Health experience to climate changes or weather events. By identifying these cross-practice data gaps, the discussion focuses on potential approaches for improving data accessibility, integration, and usability to support future pricing, reserving, and effective risk quantification.
Speaker: Peter Ott
After a brief stint as a high school math teacher, Peter Ott has been in the insurance and reinsurance industry since 2012. Over his career he has worked on admitted lines, excess and surplus products, and reinsurance. His background includes traditional pricing, catastrophe modeling, data management, stochastic reserving, and actuarial research. In his current role, he serves as Director at Howden Re. Peter is the current Vice-Chairperson for the Climate Change Joint Committee and Chairperson of the Actuaries Climate Index Work Group (ACI/ACRI) of the American Academy of Actuaries.
Speaker: Daniel Pribe
Daniel Pribe is a seasoned actuary and industry leader with expertise in balancing risk and reward, particularly within the healthcare sector. He founded Iris-Edge as a boutique consultancy dedicated to delivering focused, data driven insights that provide clients with a competitive edge. He has over 35 years of experience working in the health care field with extensive experience and expertise in strategic planning and forecasting, Value-Based-Care contracting, contracting, experience and ROI analyses, and medical plan design and pricing for MA, Commercial, and Medicaid lines of business. Dan has written or co-written several articles for the SOA and the American Academy of Actuaries and has spoken on various healthcare related topics such as healthcare reform, climate change, and end of life care in an aging world. He is currently a member of the Society of Actuaries Board of Directors. Dan received his Bachelor of Science degree in Mathematical Statistics from The Ohio State University. He is a Fellow of the Society of Actuaries and a Member of the American Academy of Actuaries.
Moderator: Marc Slutzky
Marc Slutzky an actuary with more than 50 years of experience. At the present he is a Board Member of IACA and is a Member of the Board of Trustees of The Montclair Foundation. During his career he was involved in advising insurers on capital management, mergers and acquisitions and on regulatory and tax issues. He has worked at the Equitable Life Insurance Company, the Home Life Insurance Company and most recently spent 23 years as a Principal of Milliman, Inc. He is a Fellow of the Society of Actuaries, a member of the American Academy of Actuaries and a Chartered Enterprise Risk Analyst.
Building on the American Academy of Actuaries’ paper "Climate Data: Actuarial Perspectives on Quality, Challenges, and Effective Risk Quantification", this webinar explores the current landscape of climate data through the lens of various actuarial practice areas. The presentation highlights significant issues regarding data quality and availability, specifically addressing blind spots such as the reporting lag bias in Property & Casualty loss data and the complexity of attributing Life and Health experience to climate changes or weather events. By identifying these cross-practice data gaps, the discussion focuses on potential approaches for improving data accessibility, integration, and usability to support future pricing, reserving, and effective risk quantification.
Speaker: Peter Ott
After a brief stint as a high school math teacher, Peter Ott has been in the insurance and reinsurance industry since 2012. Over his career he has worked on admitted lines, excess and surplus products, and reinsurance. His background includes traditional pricing, catastrophe modeling, data management, stochastic reserving, and actuarial research. In his current role, he serves as Director at Howden Re. Peter is the current Vice-Chairperson for the Climate Change Joint Committee and Chairperson of the Actuaries Climate Index Work Group (ACI/ACRI) of the American Academy of Actuaries.
Speaker: Daniel Pribe
Daniel Pribe is a seasoned actuary and industry leader with expertise in balancing risk and reward, particularly within the healthcare sector. He founded Iris-Edge as a boutique consultancy dedicated to delivering focused, data driven insights that provide clients with a competitive edge. He has over 35 years of experience working in the health care field with extensive experience and expertise in strategic planning and forecasting, Value-Based-Care contracting, contracting, experience and ROI analyses, and medical plan design and pricing for MA, Commercial, and Medicaid lines of business. Dan has written or co-written several articles for the SOA and the American Academy of Actuaries and has spoken on various healthcare related topics such as healthcare reform, climate change, and end of life care in an aging world. He is currently a member of the Society of Actuaries Board of Directors. Dan received his Bachelor of Science degree in Mathematical Statistics from The Ohio State University. He is a Fellow of the Society of Actuaries and a Member of the American Academy of Actuaries.
Moderator: Marc Slutzky
Marc Slutzky an actuary with more than 50 years of experience. At the present he is a Board Member of IACA and is a Member of the Board of Trustees of The Montclair Foundation. During his career he was involved in advising insurers on capital management, mergers and acquisitions and on regulatory and tax issues. He has worked at the Equitable Life Insurance Company, the Home Life Insurance Company and most recently spent 23 years as a Principal of Milliman, Inc. He is a Fellow of the Society of Actuaries, a member of the American Academy of Actuaries and a Chartered Enterprise Risk Analyst.
Members Only
AI / Data Science
Introducing AI in Pension Planning – A Comparative Study of Deep Learning and Fuzzy Mamdani Inference Systems for Estimating Replacement Rates
Introducing AI in Pension Planning: A Comparative Study of Deep Learning and Fuzzy Mamdani Inference Systems for Estimating Replacement Rates
Funded pensions have gained considerable attention as a strategy for securing supplementary income in retirement. This presentation aims to provide a comparative analysis of two methods for estimating the replacement rate: a deep learning model and a Fuzzy Mamdani Inference System (FIS). Since AI has gained considerable ground in the actuarial universe, an obvious step would be to investigate AI techniques, such as neural networks and fuzzy logic, in the realm of pension planning. Initial results indicate that these methods provide accurate estimations, warranting further analysis.
Speaker: Georgios Symeonidis
Moderator: Jennifer Alonso Garcia
Funded pensions have gained considerable attention as a strategy for securing supplementary income in retirement. This presentation aims to provide a comparative analysis of two methods for estimating the replacement rate: a deep learning model and a Fuzzy Mamdani Inference System (FIS). Since AI has gained considerable ground in the actuarial universe, an obvious step would be to investigate AI techniques, such as neural networks and fuzzy logic, in the realm of pension planning. Initial results indicate that these methods provide accurate estimations, warranting further analysis.
Speaker: Georgios Symeonidis
Moderator: Jennifer Alonso Garcia
Members Only
AI / Data Science
Hodge Conjecture Millennium Problem Solved?
In the twentieth century, mathematicians developed powerful methods to study the shapes of complex objects by approximating them with simple geometric building blocks of increasing dimension. These techniques proved so useful that they were widely generalized, producing tools that helped classify many mathematical objects—though the geometric origins became obscured, and some added pieces lost direct geometric meaning.
The Hodge Conjecture claims that for certain well-behaved spaces called projective manifolds (smooth projective algebraic varieties), the pieces known as Hodge cycles are actually rational linear combinations of geometric pieces called algebraic cycles.
Although the topic seems far from actuarial science, applications may arise through discrete Hodge theory on graphs and simplicial complexes, which turns these geometric ideas into computable linear-algebraic tools.
Speaker: Simone Farinelli
Session Moderator: Brian Fannin
The Hodge Conjecture claims that for certain well-behaved spaces called projective manifolds (smooth projective algebraic varieties), the pieces known as Hodge cycles are actually rational linear combinations of geometric pieces called algebraic cycles.
Although the topic seems far from actuarial science, applications may arise through discrete Hodge theory on graphs and simplicial complexes, which turns these geometric ideas into computable linear-algebraic tools.
Speaker: Simone Farinelli
Session Moderator: Brian Fannin