Most recent
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Emerging Topics Community: Anders vs. Shea, Part 1: Setting the Stage
Shea Parkes, FSA, MAAA, and Anders Larson, FSA, MAAA, are are joined by the organizers of the 2021 Milliman Health Practice Hackathon: Riley Heckel, FSA, MAAA, Austin Barrington, FSA, MAAA, and Phil Ellenberg. -
Transforming Group Underwriting Using Artificial Intelligence and Machine Learning
A review of opportunities for using machine learning and artificial intelligence in group health underwriting including cost predictors and predicting behavior and decisions. -
Reflecting on the 2021 “Introduction to Modeling Bootcamp” and Changes for 2022
In July 2021, the SOA Modeling Section ran an “Introduction to Modeling Bootcamp” aimed at beginner modelers and students. This article discusses last year’s bootcamp, anticipated changes for this year’s bootcamp, and how you can get involved! -
Introducing the Emerging Topics Community
Joe Alaimo, ASA, ACIA, interviews volunteer leaders in the community: Joan Barrett, FSA, MAAA; Kevin Durand, FSA, MAAA; Andrei Titioura, FSA, FCIA; and Steven Ngo, FSA, ACIA, CERA. Learn about the SOA’s new emerging topics community. -
Actuaries Can Excel® at Data Science (Pun Absolutely Intended)
We explore the use of mito, a Python package that allows users to use excel-like point-and-click interface with large datasets in Python. -
Hack-A-Thon
This is a historical account of the predictive analytics Hack-A-Thon sponsored by the SOA. -
Integrated and First Principles Modeling for Hybrid Life and Health Products
This article discusses the two main model types for modeling Hybrid Life and Health products - a claim cost model and an integrated first-principles model - and the pros and cons of each. This article also discusses how these models are constructed in practice, including common approaches and assumptions, and ways to model interplay of assumptions and sensitivities. -
Ensuring Model Wellbeing Through Monitoring
This article discusses techniques to monitor and measure predictive models for degradation in data or predictions over time. -
Using a Statistical Framework and AI Techniques to Enhance Basic Actuarial Assumptions Part 2: Application to Accelerated Underwriting
The paper uses the actuarial and statistical framework developed in Part 1 of the paper and discusses an application of this model in accelerated underwriting. It is a methodical way to quantify the impact of various risk factors in the underwriting process besides age and gender and provide an actuarially rigorous process to evaluate the aggregate risk level of a new policyholder. -
A Statistical Framework for Enhancing Basic Actuarial Assumptions Using External Data Sources and AI Techniques: Part 1
The article provides a general modeling technique to enhance base actuarial assumptions using multiple linear regression and AI techniques. The modeling framework provides a novel way to combine risk factors impacting a base actuarial assumption from various independent data sources to create an aggregate risk adjustment factor that is logical and consistent.
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Dive into insightful content. Gain practical knowledge. Explore the latest research and key information for future use. Discover the Emerging Topics Community, an online forum that focuses on three main topic areas–Modeling, Predictive Analytics and Futurism, and Technology.