Today we are joined by Simon Glairy, a recognized expert at the intersection of insurance, technology, and risk management. With a deep specialization in Insurtech and AI-driven risk assessment, Simon is uniquely positioned to shed light on the forces reshaping the actuarial profession. Our conversation will explore the pivotal challenges and opportunities facing insurers as they navigate a landscape defined by complex sustainability demands, the transformative power of artificial intelligence, and a relentless wave of regulatory evolution. We will delve into the practical realities of modernizing legacy systems, the strategic importance of AI governance, and the actuary’s expanding role as a key business advisor in an increasingly uncertain world.
With actuaries increasingly advising on sustainability frameworks and climate risk, what are the primary hurdles in modeling these complex risks? Could you provide a step-by-step example of how an actuary might guide an insurer on integrating these considerations into their strategic asset allocation?
The primary hurdle is that climate risk is a fundamentally different beast from the risks we’ve traditionally modeled. We’re dealing with deep, long-term uncertainty and often a frustrating lack of granular historical data for events of the magnitude we anticipate. It’s not like modeling mortality, where we have centuries of reliable data. This feels more like trying to forecast a complex system where the rules are changing as we play. It’s a challenge that engages every part of an actuary’s skill set.
As for guiding an insurer, a practical approach would start with comprehensive stress and scenario testing. First, we’d model both physical risks, like the increased frequency of hurricanes in a specific coastal region, and transition risks, such as the financial impact of a sudden, globally enforced carbon tax. Then, we would translate these scenarios into tangible financial impacts on the insurer’s investment portfolio, quantifying how different asset classes would perform under each scenario. Finally, this detailed analysis is integrated directly into the strategic asset allocation framework, allowing the insurer to not just react to risk, but to proactively tilt its portfolio toward more resilient, sustainable assets while managing its long-term solvency.
Many insurers are overhauling foundational infrastructure to fully leverage AI, which can temporarily stall projects. What specific infrastructure or data challenges are most common, and how can leadership ensure these essential “back-to-basics” projects deliver long-term value without derailing current business objectives?
The most common challenge I see is the “data swamp.” For years, many carriers have accumulated vast amounts of rich data, but it’s often locked away in siloed, legacy systems that don’t talk to each other. It’s disorganized and difficult to access, making it nearly impossible for AI algorithms to work their magic. This forces a necessary, but sometimes painful, pause. You can’t build a skyscraper on a cracked foundation, and for AI, clean, harmonized data is the bedrock. This often involves a complete rethinking of their cloud strategies to create a more agile and interconnected data environment.
To prevent these projects from derailing the business, leadership must frame them not as IT expenses, but as fundamental business investments. Success requires a clear, phased roadmap with measurable milestones, ensuring that business-critical functions are maintained throughout the transition. It’s also vital to celebrate the small wins along the way—like the successful migration of a single product line’s data—to maintain momentum and demonstrate tangible progress. This way, the temporary stall is seen for what it is: a crucial pit stop to refuel and re-equip for a much faster race ahead.
As insurers deploy AI for tasks like pricing and reserving, what are the key differences between treating AI governance as a compliance checkbox versus a strategic enabler? Please share a few practical metrics or cultural initiatives that differentiate firms that get this right.
The difference is night and day. Treating AI governance as a compliance checkbox means you’re always playing defense, reacting to regulations, and viewing governance as a cost center. It’s about doing the bare minimum to avoid fines. Conversely, when it’s a strategic enabler, governance is embedded from the very beginning. It becomes part of the innovation process itself, building trust with regulators and customers while unlocking the full commercial potential of AI. It’s a proactive stance that turns a potential liability into a competitive advantage.
Firms that get this right are culturally different. They often have cross-functional “AI ethics” boards that review projects from inception. A key metric they track isn’t just model accuracy, but also “explainability scores” to ensure they can justify an AI-driven decision to a regulator or a policyholder. Another initiative is continuous “bias-in-data” training for both technical and business teams, fostering a shared responsibility for ethical AI. This cultural and procedural embedding is what truly separates the leaders from the laggards.
The phased rollout of ICS 2.0 and reforms to Solvency II will test insurers’ data harmonization and reporting capabilities in 2026. What are the most significant operational challenges firms face in running management, pricing, and regulatory models simultaneously, and what should they look for in a software platform?
The most significant operational challenge is the sheer complexity and demand for consistency. Historically, an insurer might run a pricing model, a management model for internal planning, and a regulatory model on different systems, with different assumptions and data inputs. With regulations like ICS 2.0 starting on January 1, 2026, regulators are demanding a single, coherent view of the business. Trying to reconcile these disparate models after the fact is an operational nightmare—it’s inefficient, prone to error, and drains resources. You end up with a team of actuaries spending their valuable time trying to explain discrepancies rather than generating strategic insights.
To overcome this, insurers need to look for a software platform that is fundamentally integrated. The key is the ability to run these different models simultaneously from a single, consistent data source and a unified set of assumptions. The platform must be transparent, allowing users to drill down into the calculations, and fully auditable, providing a clear trail for regulators. This isn’t just about efficiency; it’s about creating a single source of truth that builds confidence both internally for decision-making and externally for regulatory scrutiny.
Rising cross-border climate litigation is a significant concern for 2026. Beyond basic compliance, what proactive risk mitigation and transparent reporting steps can actuaries recommend to multinational insurers to build a more defensible governance posture? Can you provide a specific example?
This is a critical area where actuaries must move beyond pure calculation and into strategic advisory. Basic compliance is no longer enough; a defensible posture requires proactive, transparent, and robust governance. We must advise insurers to go beyond simply disclosing their carbon footprint. This means embedding climate risk analysis into every facet of the business, from underwriting and pricing to investment and capital management. The goal is to create a clear, evidence-based narrative that demonstrates the company has diligently assessed and is actively managing its climate-related risks and opportunities.
For a concrete example, an actuary could help a multinational insurer develop a forward-looking “Transition Risk Report.” This wouldn’t just be a disclosure; it would be a strategic document. It would use sophisticated scenario analysis to model the impact of various decarbonization pathways on the insurer’s assets and liabilities. The report would transparently detail the assumptions made, the methodologies used, and, most importantly, the specific actions the board has approved to mitigate the identified risks. This creates a powerful, auditable record that shows the insurer is not just reacting but is thoughtfully and proactively governing its response to the climate transition.
What is your forecast for the actuarial profession over the next five years?
My forecast is one of profound evolution and opportunity. The core mathematical discipline of the profession will remain essential, but its application will broaden dramatically. We will see a decisive shift from historical analysis to forward-looking strategic advisory. The actuary of the next five years will be a translator, bridging the gap between complex data from AI models and climate scenarios, and the tangible business decisions that leadership needs to make. Technology will automate many of the routine tasks, but this will free up actuaries to focus on higher-value work—interpreting results, challenging assumptions, and guiding the business through unprecedented uncertainty. The most successful actuaries will be those who embrace this change, combining their technical rigor with strong communication skills and a deep understanding of the strategic landscape.
