Building AI that Actuaries Can Trust
Felix D'Alançon
Índice
In our previous article, we covered the organizational side of AI transformation: the processes, roles, and cultural conditions required for real AI adoption across the company. That led us to a more challenging question:
Once Akur8 was primed for transformation, how do we build AI that actuaries can trust?
What Makes Actuarial Work Different
As we shifted our focus to actuarial work, three realities had to stay front-of-mind as we built AI capabilities:
Delayed Error Detection
A software bug will typically surface within a matter of days. An actuarial modeling error, however, could take years to appear in loss experience, and even longer to trace back to the original issue.
Actuarial Judgement
Language models can draw on a large body of training data when writing code, but they cannot intuitively replicate the contextual, experience-based judgement that defines actuarial work.
Regulatory Scrutiny
Every AI-assisted output that flows into a rate filing, reserve calculation, or capital model introduces regulatory risk. AI must be auditable, traceable, and explainable in order to pass regulatory standards.
These three factors shaped the way any AI capability for actuarial work needed to be built.
Learning from Software Development
Because actuarial work and software development share commonalities in their processes, taking inspiration from previous lessons learned provided a strong foundation.
To avoid trading short-term productivity for long-term complexity, we developed a framework we call “Constraint Engineering”.
- The human defines the architecture, boundaries, and quality standards.
- AI generates within those constraints.
- The human reviews how components interact, rather than inspecting every line of output code.
- Automated, deterministic checks enforce the constraints before any output moves forward.
The human controls the system by controlling the constraints. When applied to our engineering process, teams achieved productivity gains of up to 50%. When applied to actuarial work, it gave us a path toward AI output that could be verified, reviewed, and trusted.
From Principles to Practice
Building an AI application is easier than ever. Building the infrastructure to run that application safely is just as hard as it has always been, and possibly harder, because the surface area for failure is larger. To apply Constraint Engineering successfully to actuarial processes, we needed two layers of control:
Infrastructure
AI models need a clean, well-governed data foundation, the business logic and institutional knowledge required for context, and the security and compliance architecture underneath it all.
Guardrails
AI models will occasionally produce outputs that are confidently wrong. In actuarial work, the equivalent of software quality assurance is a deterministic execution layer: actuarial models, KPIs, and logical checks that validate what the AI produces before a human reviews it.
What We Built: Akur8 Agents
Akur8 Agents, launched in Q2 2026, are a direct result of our organizational transformation toward becoming an AI-first company. They are AI agents designed specifically for actuarial workflows, embedded directly in the Akur8 platform.
Principle 1: Curated by Actuaries, for Actuaries
The agents are built and validated by actuarial experts. The prompts, logic, and outputs are grounded in actuarial practice, including the edge cases and judgment calls that matter in production.
Principle 2: Transparent and Traceable by Design
Every step the agent takes is logged, timestamped, and visible. There are no black-box outputs. Actuaries can see exactly what the agent did and why, which is an important requirement for regulatory environments and internal governance alike.
Principle 3: Built on a Trusted Technology Foundation
Agents run on Akur8's cloud infrastructure, on top of proprietary actuarial and statistical engines. Prompts and data are never reused for model training. The infrastructure is production-grade, enterprise-secure, and ML-native from the ground up.
This is Constraint Engineering applied to actuarial work. The AI operates within boundaries defined by actuarial logic. The outputs are verifiable because the constraints are explicit and enforceable
Akur8 runs workshops for actuarial teams exploring what responsible AI implementation looks like in practice. If you want to understand how this journey could apply to your organization, reach out through our website.
Felix d'Alançon is Chief Operating Officer at Akur8. Ludovico Capparelli is Head of AI Transformation at Akur8.
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Acerca del autor
Felix d'Alançon is Chief Operating Officer at Akur8, in charge of M&A, partnerships and company performance, as well as North America coordination. Felix started his career at the international consulting firm Oliver Wyman, where he led strategic and operational transformation projects for large companies in France and in the US. Felix is based in Akur8's New York office.
