Description: A deep dive into utilizing open-source tools for cash flow projection or valuation tasks when Excel and vendor software may not be the right fit. Speakers will cover common concerns about open-source tooling, a real-world example of Excel at its limits, a walkthrough of building a valuation engine in Python, performance comparisons of multiple languages and implementation approaches, and perspectives on using open-source tooling in the real world. Attendees will learn the pros and cons of open-source tools, when incorporating these tools might make sense, ways to get started, and gain exposure to advanced topics like profiling and optimization, concurrency, and adding a graphical user interface.

Description: Artificial intelligence brings immense opportunities for productivity gains—and valid concerns. It can both support innovations in insurance products and in the customer experience as well as enhance our profession’s work. Actuaries share a responsibility for caution and professional judgment to use it without jeopardizing the fair and equitable treatment of individual consumers and the principals we serve.AI creates significant challenges for consumers: opacity, reliable accuracy, privacy, and verification of personal data. The Academy and its members have been working to educate and inform stakeholders, insurance commissioners, and regulators toward improving the transparency of algorithms that underlie the use of AI by insurers, to explain how the algorithms can be used to achieve equitable treatment, and to identify the issues and trade-offs related to ensuring consumer protections. We must amplify the actuarial perspective on how adequate disclosures, documentation, and governance of AI can help protect users. This session will examine how we can govern and support the evolution of better practices. We will discuss how AI is addressed via standards such as ASOP No. 56, Modeling, and other guardrails that are being developed to best utilize AI’s real-world potential and fulfill our professional responsibilities.

Description: Asset liability management is an important task for any insurer, where tools like Excel play an important role from an analytics perspective. Dashboard tools like Power Bi and Tableau can supercharge an organization’s ability to perform ALM with real-time precision and monitoring of risk/imbalances. The dashboard can provide insights into potential risks posed by asset allocation and concentration, allowing for informed decision-making to mitigate those risks. The presenters will walk the audience through an example of ALM use case, including how to build key performance indicators such as cash flow, asset allocation, and risk exposure. Additionally, we will demonstrate how these types of dashboards can be customized to fit the specific needs of your organization.

Description: We explore the question of how an actuary–artificial intelligence collaboration might look in the future and how actuaries should be trained to best leverage this technology. It felt fitting to accomplish this through a conversation with an AI system, as AI involvement is warranted to ground the analysis in the technology’s true capabilities.

Description: You’ve written the position papers, updated the models, and designed the required disclosures, but you may be wondering how to explain the results during interim financial reporting periods to company management as well as other stakeholders. This session will explore ways to adapt existing management reporting and offer ideas for new metrics. We will explore various methods for analyzing DAC amortization patterns, market risk benefit reserve patterns, and Liability for Future Benefits (LFPB) movements. In addition, this session will explore ways to integrate changes in LDTI with business planning and other broader business issues such as product pricing considerations and reinsurance strategy.

Description: This session introduces you to the next level of gen AI: AI Assistants (also known as Custom GPTs, Gems, or Bots). An AI Assistant is a specialized, tailored version of a Large Language Model (LLM) that allows the user to produce a persistent, pre-programmed tool designed to handle complex, repetitive professional tasks with guaranteed consistency, accuracy, and alignment. We will move beyond casual prompting to explore the mechanical architecture required to build reliable, high-stakes assistants for a variety of applications.