Description: This is a research paper titled "Searching for Simplicity: Using Behavioral Science to make Life Insurance Product Information Simple and Effective" published by the Society of Actuaries Research Institute in August 2024. It explores improving customer comprehension of life insurance products using behavioral science.

Description: This paper offers a technical overview of the mechanics of Generative AI, with several actual examples throughout. It is intended to be a practical introduction to the concept, not a comprehensive view.

Description: Come explore an in-depth overview of telehealth to set context and navigate the continuing expansion of telehealth and its integration into the healthcare delivery system. You will examine the forces that shape telehealth offerings and expect a view of the possible futures of this evolving modality.

Description: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: This session on Innovations in Underwriting will explore the latest advancements in underwriting technology and methodologies. It will focus on how these innovations are transforming the insurance industry, making underwriting processes more efficient, accurate, and customer-friendly. Topics will include the integration of artificial intelligence (AI), machine learning, and big data analytics into underwriting workflows. Additionally, we'll discuss emerging trends in risk assessment, automation of underwriting tasks, and the implications of these changes for underwriters and insurance professionals. Attendees will have the opportunity to learn from industry experts and participate in discussions on how these innovations are reshaping traditional underwriting practices. Learning Outcomes: - Gain an understanding of the latest technological advancements impacting underwriting, including AI, machine learning, and data analytics. - Learn how these innovations can streamline underwriting and pricing processes, improve accuracy, and reduce costs. - Explore new risk assessment methods and discover how automation is changing the underwriter's role. - Identify best practices for integrating innovative technologies into existing underwriting and actuarial pricing frameworks.

Description: This session will discuss basic and advanced hedge modeling concepts. It will explain common hedge strategies used to support guarantees and options embedded within life and annuity products and the materiality of these products within the US. Attendees will learn how Machine Learning acts to reduce the computer processing needed to simulate the dynamic hedging of product liabilities projected in the future across many stochastic scenarios. Actuaries working in consulting, software development, and a large life insurance company will provide a wide range of hedging viewpoints ranging from a broad industry perspective to a hedging practitioner. Attendees will obtain a comprehensive understanding of a novel ML approach to modeling dynamic hedges and the hedge optimization opportunity it presents.

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: Who we are as people and who we become over time is entirely determined by the habits that we practice. And, habits are the fabric from which company culture is made. However, habit are hard to change, because we misunderstand what it really takes to change habits. Why? Because human beings don't have habits - habits have human beings. If you're curious about what this means, have an interest in the science of Context Design, and want to learn how to change your own habits, as well as create cultures of high performance, join this session. Learning outcomes include: - Understand the 'golden rules of habit change' and the many myths around habits (e.g., that they take a certain number of days to create). - Learn about the science of Context Design: how to create habits, at scale, over time. For yourself and your team. - See why 'human have habits' misrepresents reality and why it's more accurate to say 'habits have human beings!'

Description: Generative AI has become a hot topic since ChatGPT became one of the most well-known Machine Learning models ever. There are many different generative models out there, and in this presentation, we explore the possibilities, benefits, reservations, and alternative use-cases of using Generative Adversarial Networks to generate health insurance claims data.