Description: Welcome to the future of insurance, for actuaries and non-actuaries! Discover how cutting-edge AI models are revolutionizing underwriting, risk assessment, and customer experience for the insurance industry. Experts in management consulting and actuarial consulting firms will provide valuable insights into the consideration and challenges surrounding the adoption of Generative AI.

Description: Virtual - Session 6A: The ROI of Wellness - Actuarial Insights on Digital Health Engagement

Description: Session 9C: Adventures in AI - Group Insurance Perspectives

Description: This session shows the fascinating world of Generative AI, focusing on the principles of Retrieval Augmented Generation and Agentic Workflows. Attendees will gain a comprehensive understanding of how these advanced AI techniques can revolutionize processes by integrating external data retrieval with generative models. Practical examples and expert insights will illustrate the transformative potential of this technology in various industry applications.

Description: This session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

Description: As insurers search for ways to optimize the value of their actuaries, cloud-based data architecture has emerged as a leading solution to deliver quality data that enables the unlocking of actuarial potential. Hear an introduction to cloud based data architecture, explore cloud-based data strategies and view a live demo. Learn about different data “zones”, piping data between the zones, transforming data, and a collection of cloud-based tools that could be used for each one of these steps.

Description: Model runtime is a common challenge for actuaries in the life and annuity space. In particular, applications such as pricing and forecasting may require the use of nested-stochastic calculations, which may require extensive computing resources or use of rough approximations. There are also other areas where runtime may be a challenge, such as projecting certain reserves or layering solving algorithms within actuarial calculations. Recently, runtime challenges have been exacerbated by the development of increasingly complex insurance products and changes in reporting frameworks, such as IFRS 17, LDTI, PBR, and Solvency II. Many approaches have been explored by actuaries in the past to address model runtime challenges. To further this research, the SOA has recently published the paper Predictive Analytics and Machine Learning - Practical Applications for Actuarial Modeling. The authors of the paper will share their insights on innovative techniques using the implementation of artificial intelligence and machine learning (AIML). Come examine an introduction into AIML, review how AIML can be used to circumvent nested stochastic and other runtime challenges, explore case studies, review how costs and benefits can be measured and discuss model risk management considerations. Key learning objectives include: - Understanding the fundamental concepts of AIM - Understanding applications of AIML for actuarial modeling - Learning about the costs and benefits - Learning how AIML can be applied in practice to improve model runtime

Description: Behavioral economics research has shown that small details can have a large impact on how a person behaves. As life insurance continues its evolution into the digital domain, life insurers now have an almost infinite number of ways to tweak their product offering, positioning, and delivery to the end consumer. Behavioral science is becoming increasingly more important to optimize these product design decisions and improve outcomes for both insurers and their customers. Discover a brief overview of key behavioral economic concepts and dive into two SOA research projects that are exploring how behavioral economic techniques can be used in different parts of the life insurance value chain. First, you'll start before a policy is even sold, by exploring how behavioral economics, through techniques such as the messenger effect, can be used to increase consumers' comprehension of policies and life insurance's value. Second, you'll look at how underwriting question design can impact applicant behavior and lead to higher quality application data. At the conclusion, you'll enjoy having a better understanding of how behavioral economic techniques can be applied in a life insurance context.

Description: Discover a modernization journey within finance, actuarial investment and risk domains. Expect to learn about the world we are striving for - an event-based, self-service, automated environment that centers around a cloud-based data lake with proper data quality and governance and provides visual, on-demand solutions for reporting and analysis. We will educate the audience on the technology used to obtain this vision and the approach (including our team structures and project management ideologies) we have taken thus far to start moving there.

Description: Join a discussion on the evolution of asset and liability projection processes in the life insurance industry. Familiarize yourself with the broad set of inputs, stakeholders, and financial planning and analysis use cases. Notable areas of focus will include, but are not limited to, historical background, modeling needs, interactions between stakeholders, industry pain points, and a composite of an ideal future state informed by perspectives gleaned from leading companies within the industry.