Actuarial and underwriting AI that satisfies regulators.
Solvency II and IFRS 17 require that every number in a regulatory submission be auditable. When AI produces those numbers, the audit requirement follows. xflow provides the metadata lineage that makes AI outputs regulator-ready.
The explainability gap in insurance AI
Actuarial and underwriting models already sit inside strict evidence and sign-off processes. Adding AI to those processes creates a specific obligation: showing which assumptions, rules, and data lineage shaped each output.
The issue is not whether actuarial data is governed. The issue is whether AI-assisted outputs can inherit the same assumptions, lineage, controls, and sign-off evidence as the process they are entering.
Three insurance workflows where governed context matters
Each use case starts from the metadata your actuarial and data governance teams have already built. xflow converts that into executable context for AI, with lineage that satisfies regulatory scrutiny.
AI-assisted capital calculations with auditable lineage
Solvency Capital Requirement calculations involve risk aggregation across multiple modules. Any AI-assisted scenario, stress test, or sensitivity run has to preserve the input assumptions, calculation steps, and supervisory evidence behind the result.
xflow converts your capital model metadata — risk parameters, correlation matrices, regulatory definitions, assumption libraries — into executable context that AI operates within. Supervisory review of AI-assisted SCR calculations starts from a complete context evidence record.
Actuarial reporting automation with traceable assumptions
IFRS 17 requires the assumptions behind insurance contract measurements — discount rates, risk adjustments, cash flow projections — to be documented and auditable at each reporting period. If AI is used in the calculation chain, the same evidence standard applies to every output it influences.
xflow maps your actuarial assumption libraries, grouping methodologies, and discount curve metadata into executable context. AI-assisted IFRS 17 calculations carry a complete record of which assumptions were applied, from which version of the assumption library, under which accounting policy.
Underwriting AI with explainable risk decisions
AI-assisted underwriting is classified as high-risk under the EU AI Act: decisions that affect insurance pricing or coverage terms require human oversight, technical documentation, and the ability to explain any individual decision. Without a context layer, meeting this standard requires a separate compliance exercise for every AI-assisted decision.
xflow converts your underwriting rule libraries, pricing factor metadata, and product definitions into executable context that AI operates within. Every AI-assisted underwriting decision is traceable to the specific pricing logic and data definitions applied — satisfying both internal governance and supervisory requirements.
Three owners. One shared problem.
The context layer for AI spans the actuarial, risk, and data functions. Each accountable function reaches the same conversation from a different direction.
Chief Risk Officer / CRO
For risk leaders responsible for Solvency II and internal model validation, xflow extends established governance controls into AI-assisted calculations. The benefit is clear evidence of which assumptions, rules, lineage, and approvals shaped each output.
Chief Actuary
For actuarial leaders owning IFRS 17 assumptions and sign-off, xflow keeps assumption lineage attached to AI-assisted calculations. Reviews can start from the governed context record rather than a separate reconstruction exercise.
CFO / Head of Finance
For finance leaders accountable for financial statement accuracy and audit readiness, xflow gives AI-assisted reporting outputs an evidence trail: source data, assumptions, transformations, controls, and context version in one place.
See xflow for your insurance use case
Tell us your regulatory context — Solvency II, IFRS 17, underwriting AI, or transformation. We will walk you through what executable context looks like for your specific programme.