Redefining the Actuary's Edge in the Age of AI

This webinar is a fireside chat between actuaries from EY, Oliver Wyman, and Coherent on how AI and automation are changing actuarial work in commercial insurance. The conversation covers where actuarial teams are spending too much time on manual processes, how new tools are closing the gap between actuarial and IT, and what responsible AI adoption looks like in practice. It's less about AI hype and more about what's actually shifting in how actuaries model, price, and collaborate across the organization.

AI is not replacing the core of what actuaries do. It's reducing the time spent on repetitive tasks like data cleaning, re-rating, and manual spreadsheet work, so actuaries can focus more on modeling, pricing decisions, and risk analysis. The panelists discuss how the skills gap to programming and data science is shrinking fast. Actuaries won't need to write all their own code much longer, but they'll still need to know what models to build, how features should be engineered, and how to interpret results. The webinar explores what this shift means for how actuarial teams are structured, how they work with IT, and where they should be investing their time.

No, and the panelists are direct about why. AI can automate processes and generate outputs faster, but it can't replace the judgment, regulatory knowledge, and business context that actuaries bring to pricing, reserving, and risk selection. As Jeffrey Durham from EY puts it, even when AI can build a model for you, someone still needs to decide how it should work, whether the results make sense, and how to apply them to a specific book of business. The webinar discusses why the actuaries who will be most valuable going forward are the ones who learn to work alongside AI tools, not the ones who try to compete with them.

The webinar covers a practical framework for AI governance in insurance, organized around six areas: accountability, transparency, privacy, fairness and bias, security, and third-party risk. Brett Nunes from Oliver Wyman walks through how carriers can think about each pillar without overengineering their approach. The key recommendation is to start small with pilots, scale governance to the size of the organization, and build cross-functional teams (including actuaries, IT, legal, and compliance) so that AI decisions reflect multiple perspectives. The panelists stress that governance should enable safe experimentation, not block it.

This is one of the more practical parts of the discussion. The panelists explain that actuaries think in terms of risk and data while IT thinks in terms of systems, and that disconnect often creates bottlenecks during rate changes and model deployment. The recommended approach is to build cross-functional squads that include actuaries, data engineers, and product owners, so each group has visibility into how the others work. The speakers also discuss how tools that translate actuarial logic into API-ready formats (rather than requiring IT to reimplement it) give actuaries more ownership over their rates and reduce the back-and-forth that slows things down.

This session is designed for insurance professionals involved in actuarial pricing, modeling, and product development, as well as leaders in actuarial operations, IT, and innovation who are thinking about how AI fits into their teams' workflows. It's especially relevant for pricing actuaries in P&C insurance, actuarial managers evaluating new tools or governance frameworks, and IT leaders who work closely with actuarial teams on model deployment and integration. If you're trying to figure out where to start with AI adoption (or how to move past the pilot stage), this conversation gives you a practical frame of reference.

Yes. This session qualifies as an organized activity under U.S. Qualification Standards for Actuaries and may provide up to 0.5 CE credits.