Description: "As digitization and automation rapidly progress in today’s life and health industry, issues with misrepresentation during the insurance application process are also becoming a growing concern for insurers around the globe. A recent survey finds that misrepresentation in application forms costs the average insurance customer an estimated 5% to 10% in higher premiums. In order to reduce this risk, insurers must find and correct areas of misrepresentation, consult post-issue sampling, review the automated underwriting processes, and monitor the quality of financial advisory firms. Learn how predictive disclosure modeling detects and reduces misrepresentation by harnessing readily available underwriting data to analyze issues, helping insurers understand various drivers for disclosure rates and areas of misrepresentation.Advantages of this modeling to various stakeholders in the life insurance journey include improved risk selection, claims experience, and reinsurance terms for insurers. For customers, it leads to increased certainty of coverage, payout of the claim, and ultimately lower premiums on average. By the end of the session, attendees will understand: The disclosure modeling process at a high level The technical details of a standard approach to disclosure modeling: data cleaning, standardization, visualization, and predictive modeling Success stories about how predictive disclosure modeling has impacted insurers"

Description: Focus on Accelerated Underwriting (AUW) in the life insurance industry, which is an expedited underwriting process that utilizes predictive models. Expect to address the challenges in designing the process, from modelling to performance monitoring. Additionally, you'll address risk management, particularly in mortality and proxy discrimination andhighlight the role an actuary can play in the AUW process, as well aswhat they should know to contribute effectively.

Description: In this wide-ranging session, expect updates on the work of the Individual Life Experience Committee and delve into some interesting recent research. You'll gain an update on recent findings from the Individual Life Experience Committee regarding individual life mortality experience.

Description: Electronic Health Records are here to stay. Take a deep dive into the world of electronic health records and how the industry is poised to utilize this new form of digital medical data to transform the way policies are underwritten and ultimately, transforming the client experience. Listen to industry experts speak about what they are doing to use this data most effectively and how this ultimately develops into ground-breaking solutions for the industry.

Description: Session 4B: Impact of GLP-1 Drugs on Health Care Cost Trend and Clinical Outcomes

Description: Virtual - Session 4A: Data Science-Driven MA Product Design Strategies

Description: Join us as we delve into the dynamic landscape of the Fixed Indexed Annuity industry. Presenters will discuss trends observed in recent FIA experience, observations from their own company's data, and findings from Milliman's FIA industry experience studies. Key results include a view of surrender behavior relative to market dynamics and comparisons with industry predictive models, serving as a benchmark to explore where industry experience aligns with expectations and where potential surprises have emerged. Through a combination of data-driven analysis and expert interpretation, attendees will gain valuable perspectives on the evolving FIA landscape, enabling them to make informed decisions to navigate future challenges.

Description: Virtual - Session 8D: An Application of Clustering Approaches in Value-Based Care

Description: As health insurance self-funding becomes more popular among smaller group sizes, risk is evolving for the MGUs, stop loss writers, and reinsurers that cover them. Assessing more small groups at lower attachment points means rethinking traditional stop loss underwriting techniques.

Description: In this presentation, actuaries and data scientists from Cleveland Clinic and Milliman discuss the development of a sepsis prediction model using machine learning. Presented with a unique challenge of handling massive amounts of data for near real-time risk scoring, the team took their model development to the cloud and delivered a product in less than a week through a unique virtual engagement.