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: A brief overview of recent AIT research papers in summarized format.

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:

Description: FASB has provided broad guidance for application of long-duration targeted improvements (LDTI) in Actuarial Standards Update 2018-12 for U.S GAAP financial reporting. However, the broad guidance provided leads to several interpretations and technology, data and system implementation challenges. During this session, the presenters will focus on sharing the technology, system and data challenges and insights gathered from their FASB LDTI implementation journey, including lessons learned from collaboration with other discipline areas and stakeholders such as finance, investment and IT. The presenters will also discuss methods to overcome challenges and go beyond compliance.

Description: Companies with the intention of being the top, trusted source(s) in an information overloaded world.

Description: Support systems and new solutions to enable caregivers to deliver better care and reduce personal stress.

Description: Feature article that contends in order for actuaries to thrive, technology needs to be central to company strategy and actuaries should view coding as a core competency—equal to or more important than other “traditional” skills.

Description: LDTI’s introduction has forced many insurers to examine their existing operations and technologies. They must determine how to optimize compliance efforts while balancing data, analytics and system modernization considerations to achieve maximum value. This article will examine the four key facets—the data, technology, processes, and people—essential to any successful LDTI exercise. It will also touch on the various approaches for tackling LDTI and how a modernization mindset can help insurers realize much greater transformation for the business.

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.