Description: Syllabus for Statistics for Risk Modeling Exam—May 2026

Description: Learning more about the Pre-Actuarial Foundations Module by reading the module introduction and objectives.

Description: Review the passing candidate list by number for SOA’s January 2026 administration of exam SRM.

Description: Summary of an expert roundtable on generative AI in healthcare and health insurance, focusing on use cases, governance, trust, risks, and evolving actuarial skills.

Description: This session will provide an in-depth exploration of model governance, with a particular focus on its relationship to model architecture and its role in fostering resilient, scalable models. We will examine key architectural principles—including scalability, flexibility, modularity, and standardization—and their impact on model reliability and maintainability.We will also highlight the benefits of conducting Architecture Reviews and introduce a development philosophy centered on writing resilient code, building trustworthy models, and implementing structured governance to mitigate risks and technical debt.Beyond architecture, we will take a broader look at model governance, outlining key roles, approval processes, and documentation standards that promote accountability and transparency. Drawing from real-world experience, we will share practical strategies for operationalizing governance and architectural best practices within a production environment, highlighting how these approaches support sustainable, scalable modeling frameworks.The session will also explore the consequences of weak governance—such as inefficiencies, compliance gaps, and audit vulnerabilities—underscoring the importance of proactive, structured oversight.Additionally, we will cover the latest regulatory updates relevant to actuarial model governance, including OSFI’s Draft Guideline, updates in AI-related regulation, and the BMA’s Consultation Paper on model validation.By the end of the session, attendees will gain practical insights into building and implementing governance frameworks that enhance model oversight, support regulatory compliance, and drive long-term sustainability in actuarial modeling.

Description: "Modern actuarial work, being highly data-intensive, offers many opportunities for actuaries to apply the statistical skills gained through their basic training. Advances in computing allow us to analyze vast quantities of data efficiently, and many software packages provide statistical 'answers' to almost any question an actuary might ask. However, in this field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later. At the conclusion of the session, attendees will be able to avoid drawing inappropriate statistical conclusions while doing their work. They will leave informed about the types of pitfalls that exist and where those might come into play in actuarial work."