The insurance industry is caught in an AI gold rush, but not everything that glitters is fit for actuarial work. As agentic AI shifts from hype to real-world deployment, pricing teams face a decision that will shape how they operate for the next decade: adopt the right tool for the job, or inherit a black box at the centre of their most heavily regulated process.
According to AKUR8, actuarial work carries a uniquely high bar, balancing three pressures that few other professions face at once. First is regulatory accountability, as pricing, reserving and capital models are submitted to regulators and signed off by certified professionals, meaning unexplainable results carry legal and compliance consequences.
InsurTech firm AKUR8 recently talked about agentic AI for actuaries, what it is and why it matters.
Second is financial materiality, since a flawed model can misprice risk and trigger adverse selection across an entire book of business. Third is the explainability standard, as actuaries must justify and defend every assumption, meaning a black box output is unacceptable no matter how accurate it appears. While most professions can tolerate AI tools that are occasionally opaque or wrong, actuaries cannot.
Understanding the AI landscape helps clarify why. Predictive AI, the first level, took actuaries beyond spreadsheets with multivariate models forecasting loss ratios, churn and underwriting tiers.
This is where Akur8 led the transformation in insurance, replacing legacy tooling with ML-powered pricing that paired statistical rigour with transparency. Generative AI, the second level, moved from prediction to production, creating reports, code and analysis from natural language, but its opaque decision-making and tendency to hallucinate remain a fundamental barrier for professionals whose outputs carry regulatory weight.
Agentic AI, the third level, is where genuine transformation begins. These systems plan, decide and execute multi-step tasks with a degree of autonomy, moving from answering questions to completing missions. McKinsey’s State of AI 2025 report found 62% of organisations are at least experimenting with AI agents, while 23% report scaling agentic systems in at least one function. Yet no more than 10% of respondents in any given function say their organisations are scaling agents broadly.
The adoption gap in insurance is striking. Agent adoption is scaling fastest in risk, legal and compliance functions (16%) and knowledge management (16%), yet product and service development and strategy, where actuaries arguably deliver the greatest impact through tariff design, pricing and capital modelling, show adoption of just 0-2%.
That gap does not suggest agentic AI is irrelevant to pricing actuaries; it suggests the right tools have not existed. Pricing demands deep knowledge of GLMs and GBMs, complex interactions, overfitting risks and regulatory frameworks, none of which generic large language models understand. Without proper human checkpoints, a flawed early assumption can compound across iterations, affecting model structure, variable selection and ultimately the rates policyholders pay.
Used correctly, agentic AI should free actuaries from execution rather than remove them from judgment. That means automating tedious rating structure updates, generating reports from reusable templates, guiding formula building, flagging variable inconsistencies before modelling, enabling multilingual collaboration and importing competitor rate filings to benchmark strategy.
This is the gap Akur8 Agents are designed to close. Unlike repurposed enterprise LLMs, they run directly on Akur8’s platform infrastructure, grounding outputs in the same ML-native foundation that powers its pricing platform. Prompts and data stay secure and are never fed into model training, every action is visible, every result traceable, and nothing changes without approval. It is AI built from the ground up, curated by Akur8’s actuarial and product teams, around the workflows and standards actuarial work actually demands.
Read the full AKUR8 post here.
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