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: With the growing threat of cyber incidents, organizations and governments are rightfully concerned. In recent years, cyber incidents have caused significant losses to entities and insurers across the world. Therefore, there is a need to understand and provide greater context around the topic of catastrophic cyber risk, which has implications for insurance companies, reinsurers, regulators, consumers, and society. Taking a multi-disciplinary, holistic approach, an expert panel study was conducted on catastrophic cyber risk in this four-part series of discussions and subsequent reports.

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: 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.

Description: This literature review provides insights on emerging technologies that interact with the actuarial profession within the space of microinsurance for the purpose of establishing a frame of reference for actuaries to use in their work. Furthermore, it intends to spark readers’ interest in, and create a vision for, how microinsurance and its associated technologies may impact the actuarial industry in the future.

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: The Actuarial Innovation & Technology Program Steering Committee (AITPSC) is seeking researchers to produce a report that discusses the life cycle of innovation and how to keep up with the increasing pace of change in our field. Research format should include interviews and case studies from across the actuarial profession.

Description: Artificial intelligence is rapidly transforming the investment landscape. From natural language processing and large language models to physical AI applications like robotics, autonomous vehicles, and drones, the evolution of AI is reshaping both how we analyze data and where we find opportunity. We’ll begin by exploring the evolution of AI, which is already driving disruption across industries such as coding, customer service, and marketing. However, the future of AI is physical AI (drones, AVs, robotics). With valuations already stretched, and growing concerns about the “circular economy” and bubble risks, investors should focus on high-quality tech companies that generate free cash flow and deliver strong returns on invested capital. We'll explore examples of interesting companies across the globe: Meta, Alphabet, and Uber in the US; ASML in Europe; Tokyo Electron in Japan; TSMC in Taiwan; and BYD in China. The session will also examine how natural language processing and large language models are reshaping the way quantitative and fundamental managers extract insights from raw data. We’ll highlight how these tools are being deployed to enhance understanding of risk and return, and how they’re increasingly supporting collaboration between quant and fundamental teams. Original Content: