The AI Landscape — What Actuaries Need to Know

Abstract

Two pricing actuaries with identical fellowship qualifications can now produce radically different output on the same 340-account renewal: one delivers indications for 60 accounts in a week; the other, supported by an AI system handling extraction, normalisation, and first-pass analysis, has the full portfolio ready for peer review by Tuesday. The difference is not talent — it is workflow, and it frames the question this article answers: what does AI actually mean for actuarial work, stripped of both breezy reassurance and breathless prediction? Our thesis is structural, not speculative. AI is the next computational tool in a sequence the profession has absorbed for two centuries — mortality tables, GLMs, and Chain Ladder projections have long performed the core functions of pattern recognition, prediction, and adaptation; what modern systems add is the ability to process unstructured information. Adoption is being forced by four compounding pressures: regulatory complexity (IFRS 17, the Solvency II review, LDTI), data volumes beyond human processing limits, competitive pressure from AI-native entrants, and a persistent talent gap. To navigate them, we set out a taxonomy of five AI system types — rule-based, machine learning, deep learning, generative, and agentic — matched to the workflow stages each suits, and a division-of-labour framework anchored in a boundary that is permanent rather than temporary: no AI system can sign a Statement of Actuarial Opinion or bear professional liability. AI processes; the actuary decides.