Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

Description: The SOA Research Institute and LIMRA have completed the most up-to-date study of variable annuity contract owner behavior. Learn more about this vital resource for actuaries working with annuities.

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: Almost half of recent FDA approvals have been for orphan drugs. This trend is expected to continue in 2023. Orphan drugs are priced more than 30 times higher than non-orphan drugs, therefore their cost burden in aggregate has the potential to overwhelm the budgets of payers, especially that of smaller health plans. Hear how the cost impact analysis for smaller health plans require different considerations than for a larger health plan when modeling the impact of rare diseases.

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: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

Description: SOA and LIMRA request mortality data for deferred annuities issued 2015–2024 to support an updated industry experience study.

Description: In this session, attendees will understand the role of AI in risk management and underwriting. You will see insightful case studies highlighting AI-driven techniques for enhancing precision and efficiency in these areas. Additionally, the session will cover advanced AI applications in claim management and fraud detection, showcasing cutting-edge strategies to mitigate risks and streamline operations.

Description: Understanding Asian Capital Regimes

Description: "This talk will evaluate the present pace of evolution in actuarial data management due to economic and demographic pressures, evolving regulatory/compliance requirements, AI and the growing need to have advanced analytic capabilities. We will evaluate each factor briefly to isolate their impact on actuarial data management, highlighting risks and opportunities for actuaries and IT teams supporting actuaries. Attendees will gain insights into the factors affecting changes in actuarial data technologies, understand key differences between centralized and decentralized data management approaches, including their impact on data quality, governance, performance, and scalability. The session will highlight some pain points of centralization, such as the integration of actuarial transformation initiatives with IT processes, juggling data complexity and business logic, and the limited creativity and agility due to interdependencies between components and evaluate how emerging concepts such as actuarial data stores tackle the 'centralization' problem. We will also cover a real-life use case covering the evolution of actuarial data management and challenges identified in previous parts of the presentation. The session will avoid going into any specific solution details: it will address current challenges due to broad shifts, then evaluate newer concepts, corresponding risks and opportunities. - Understand key macro and micro drivers impacting actuarial data management - Understand centralized and decentralized approaches to actuarial data management - Evaluate the concept of an actuarial data store - Coordinate better with IT teams to deliver changing actuarial data technology needs"