SOA Survey: AI Adverse Outcomes Top Risk for Insurers
The Society of Actuaries released its 19th Annual Survey of Emerging Risks on March 10, 2026, and artificial intelligence adverse outcomes ranked near the top of the list for insurance and financial services leaders. The survey, which had more than 350 respondents including over 100 C-suite and executive-level professionals, found that technology risks broadly dominated executive attention, with AI specifically identified as a leading concern.
The result is not surprising, but it is worth paying attention to because actuaries are trained to measure risk rather than chase trends. When actuaries rank AI adverse outcomes alongside financial volatility and geoeconomic shifts, it signals that AI is no longer viewed primarily as a cost-saving or innovation opportunity. It is a risk that can affect balance sheets, reserves, and reputations. That shift in framing is what makes the survey more than a headline.
The survey distinguishes between the promise of AI and the downside of getting it wrong. Insurers have been quick to adopt predictive models for underwriting, pricing, claims, and fraud detection. The SOA results suggest that the next phase of risk management will focus on the consequences of model errors, data drift, biased outputs, and the difficulty of explaining automated decisions to policyholders and regulators. These are not theoretical problems; they map directly to market conduct exams, litigation, and regulatory enforcement actions that are already happening.
For carriers, the survey is a prompt to align AI governance with enterprise risk management. Models that affect underwriting or reserving should be treated like catastrophe models or economic scenario generators: subject to validation, independent review, and documented assumptions. The difference is that AI models change faster and often depend on vendor platforms that the carrier does not fully control. The SOA results suggest that the next wave of actuarial guidance will focus on how to model and reserve for AI-related operational risks, which until recently were treated as IT issues rather than balance-sheet concerns.
The SOA findings also suggest that the risk conversation should include the people who use the models, not just the data scientists who build them. A claims adjuster who follows an AI recommendation without understanding its limits is a different risk than a model with a coding error. Governance must address both the technology and the workflow around it. Carriers that only validate models in isolation, without observing how frontline employees interact with them, will miss a major source of operational risk.
The survey also has implications for capital and reserving. If actuaries, who are central to how insurers set reserves and price risk, view AI adverse outcomes as a top emerging risk, then boards and risk committees should expect more detailed questions about AI risk appetite, model risk tolerance, and contingency plans for AI failures. The answer cannot be a generic policy; it needs to be specific about which systems are high-risk, how often they are monitored, and what happens when monitoring reveals a problem.
For a framework on how insurers are building governance around AI risk, see our guide to AI governance in insurance.