Description: Using the Cox proportional Hazards Model to improve underwriting debits and credits.

Description: Read the December 2018 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section.

Description: Predictive analytics and technology have dramatically enhanced insurers’ ability to write Life Insurance policies without the need for medical exams in the past 10 years. Rapid, remote access to richer, digital health data has helped better identify higher risk individuals and capture anti-selective behavior, allowing policies to be issued at rates more competitive than traditional guaranteed issue products. Yet this underwriting technology is still new and rapidly evolving, challenging insurers to develop appropriate assumptions for new issues as well as the valuation of in-force blocks.In this session, we will look back at mortality and persistency experience data gathered in Oliver Wyman’s industry-wide VM-51 experience study, one of the most comprehensive data sets to include limited underwriting business in recent years. We will see how experience held up against expectations, and what possible trends we see across various policyholder characteristics. We will then zoom in on the impact of risks that would be considered uninsurable from a full underwriting perspective, and the impact that these lives are having on in-force blocks from a valuation perspective. Finally, we will look forward, examining some of the newest innovation in the limited underwriting space, as presenters discuss methods and considerations for the validation, assessment and implementation of new underwriting offerings, such as Milliman Risk Score and LabPiQture™.Attendees will enhance their understanding of the impact of underwriting changes over the past decade, as well as have a view of how things may change in future years as the industry becomes progressively more capable of assessing risk without the need for medical exams.

Description: The life insurance industry is facing a rapidly changing landscape, with several emerging risks demanding the attention of actuaries and industry professionals. While actuaries commonly examine historical experience to guide our estimates of future outcomes, we are often faced with evidence that some of the forces that dictated past performance no longer persist and in some instances may even turn into contrary forces. This session aims to explore the key challenges and opportunities presented by risks with limited historical context, and discuss strategies to effectively manage them. We will delve into critical emerging risk areas, such as interest rate and asset risk, the rise of AI, climate change, predictive analytics in underwriting, and more. The session will feature speakers who are experts in managing risk, as well as results from interviews with top corporate risk professionals, such as CROs and ERM leads from major insurance and reinsurance companies. Attendees will gain valuable insights into emerging risks and practical strategies to navigate the evolving life insurance landscape.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: This article provides an overview of the current Artificial Intelligence ("AI")/Machine Learning ("ML") regulatory landscape and governance considerations for insurers deploying AI/ML for their long-term care business.

Description: Pandemic modeling and its impact on the insurance and reinsurance business have been the subject of scrutiny during the COVID-19 pandemic. As the World Health Organization (WHO) has declared its end as a global health emergency, the focus of insurers and reinsurers turns to the possibility of future pandemics. Key to considering possible future pandemics is assessing what is needed to model pandemic risks. Our presentation will focus on providing you with the following: - The main principles around creating a well-established pandemic risk modeling framework, what they are and how you should build them into your model (As a reinsurer, this is core to our business, and we have unique global insights into performing it.) - A review of pandemic risk modeling strategies as well as an understanding of the inherent uncertainties of pandemic events - Lessons learned from COVID-19 and how we can apply them in assessing the possibility of future pandemics - An understanding of the link between future pandemic risk and the capital requirements

Description: Hear about some applications using quantile regression-derived individual and group claim distributions and how they may be used in various traditional health care modeling situations. The session will not focus on the technical use of quantile regression to obtain estimates of claim distributions, but more so is intended to focus on the uses of the distributions in actuarial modeling and the additive framework in assessing claims distributions. Presenters will show how actuaries can better assess risks by focusing on the individual- and group-specific claims distributions as opposed to traditional point-estimate-centric models.

Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.