Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time

Description: Principle-based reserving requires modeling assets and liabilities across a range of scenarios to assess the sufficiency of assets when determining reserves, a major shift from the current formulaic framework. These changes will require valuation actuaries to evolve their skill set by heightening their focus on creative analysis, gaining deeper understanding of complex projections and clearly communicating drivers of reserve movements to stakeholders in order to influence strategic decision-making. The panelists will discuss the way that they see the role of the valuation actuary changing in response to the change in focus in reserving requirements. They will also discuss successful characteristics along with best practices for understanding and explaining results in the new paradigm. The audience will be given the opportunity to interact through polling and facilitated discussion of their stories, experiences and observations.

Description: This session is a primer on how to apply Gestalt psychology to display quantitative information to improve neural receptiveness. While much of brain functioning is still a mystery to psychologists and neuroscientists, we do understand a great deal about which parts of the brain engage or disengage given visual cues. Actuaries are often tasked with presenting very technical information and are prone to focus more on the substance of numerical data rather than a pictorial presentation based on neural preferences. This presentation will discuss how to display quantitative information more effectively tailored to these preferences.

Description: An update on the Modeling section activities and a look back at the five years since inception

Description: This research report educates the actuarial community about the various shades of the flood risk as well as the state of the art in quantitative flood risk modeling.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

Description: New web page that describes the new e-learning FSA module Scenario Modeling. It replaces Financial Reporting Module in the QFI track.

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: This article lays conceptual foundations associated with projecting investment-related discretionary elements of various life insurance and annuity products. It outlines potential implementation approaches that may be appropriate when calculations are performed in a risk-neutral setting. The article concludes that crediting rates in risk-neutral models can be projected as in real-world models, except that the modeled provision for expected credit losses should be modified to equal real-world expected losses initially, but over the projection reflect management’s evolving expectation, moving ultimately to risk neutral expected losses.

Description: Mandatory implementation of life principle-based reserves (PBR) is just around the corner, and there is no shortage of work to do, as most products have yet to be moved to PBR. Oliver Wyman recently completed its 2019 PBR survey, with more than 40 participants covering 85 percent of the individual life market, including 23 of the top 25 life writers and five reinsurers. This article will expand on the key survey findings including implementation status and model simplifications.