Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: This article delves into the transformative realm of Business Intelligence (BI) tools. We explore the fundamental functionality and structure of these tools, and through case studies illustrate how they offer valuable insights into carrier experience and underwriting dynamics. From real-time data visualization to in-depth data analysis, discover the essential features that empower businesses to make better informed decisions.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: The Mortality Improvement Model, MIM-2021- V3. Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the MIM Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement.

Description: Risk Adjustment continues to be a hot topic in Health Insurance since its emergence. This mechanism came into being as a means of reducing the risk of selection, particularly in marketplaces where underwriting is not allowed, and rating factors are significantly restricted. Risk adjustment serves to adjust premiums to reflect the risk of individual members or overall members as a whole within a particular cohort of a health plan. Risk adjustment is not perfect, as it does not perfectly predict future costs or perfectly adjust for the differences in expected costs. Different marketplaces have different challenges with risk adjustment.

Description: Methods and assumptions used in the valuation of savings by external care vendors

Description: Description and welcome to the 2020 Canadian Reinsurance Conference including highlight of the key note speaker

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: Summary of the SOA research project on PBR Simplified Methods. Describes the multi-risk scenario generator, central estimate assumptions, assumption objectivity measures, and regulatory demonstrations per VM-20.

Description: The February/March 2016 edition of The Actuary magazine—the Society of Actuaries’ flagship publication.