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Modeling Extreme Events
A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution. -
Exploiting While Exploring: An Introduction to Bayesian Optimization
An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context. -
Pandemic Shines a Light on Technology Needs of Actuaries
The COVID-19 pandemic has disrupted the global economy at a level never imagined. The economic fallout has been catastrophic. Record high unemployment, staggering revenue loss, bankruptcy and an uncertain future have businesses—and consumers—searching for a beacon of hope. In the quest for a lifeline, actuarial teams are quickly being thrust into the spotlight. -
Bridging the Gap—Actuarial and IT
Actuaries and their IT counterparts are required to collaborate more frequently and on more complex projects than ever before. The recent accounting changes (LDTI, IFRS 17, PBR), investor demands for increased transparency, and competitive pressures have led to many insurers undertaking large actuarial and financial transformations. As a result, senior management is challenging their actuarial and IT leaders to develop new processes that use data in new and improved ways. The actuarial and IT functions are being tasked with not only delivering on changes to systems, processes, and data, but also with rethinking how their teams collaborate and interact. -
Back to the Futurism—New and Improved!
Feature article discussing futurism and a study that contains in-depth descriptions of the Delphi and TIA methods; listings of the rationales and thought processes, the plausible future developments that could influence the values of these four economic variables and the resulting “fan of possibilities” for the values of these variables. -
GAAP Long-Duration Targeted Improvements: Whether Largely a Compliance or Modernization Exercise, the Considerations for the Modeling Actuary are Numerous
This article explores some key challenges facing the modeling actuary related to LDTI implementation and shares associated compliance and modernization considerations. -
Keep Up With the Standards: On ASOP 56, Modeling
A high-level review of the newly effective U.S. Actuarial Standard of Practice 56, Modeling. -
Machine Learning in the Cloud – Part 1, Intro to the Cloud
Join hosts Anders Larson, FSA, MAAA, and Shea Parkes, FSA, MAAA, for the first in a series of podcasts focused on machine learning in the cloud. This episode introduces a useful definition of the cloud and digs deeper into what aspects of machine learning make it a good fit for cloud based solutions. -
Explainable AI
Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model. -
Insights Into Life PBR Modeling Practices
Oliver Wyman recently completed its 2020 Life PBR Emerging Practices survey, with results providing a broad industry perspective on implementation impacts, strategy, assumptions and challenges. This article provides further insights into the trends and drivers observed around planned future refinements to PBR models.
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