Datum for insurance.
Datum accelerates the deployment of the data operating model in insurance — with a Common Data Insurance aligned with ACORD and an actuarial Business Layer ready for Solvency II, IFRS 17 and DORA.
Insurance data exists. The problem is that it is not governed or coherent across business areas.
Solvency II requires data used in SCR and MCR calculations to be complete, accurate and traceable. Without a governed data model, every regulatory close is a race against time.
IFRS 17 requires granularity and coherence in insurance contract grouping. Actuarial, accounting and operational data must speak the same language — something that rarely happens without a common model.
Fraud detection and dynamic pricing models depend on data quality and historical consistency. A model trained on poorly governed data produces decisions nobody can explain or defend.
Actuarial, accounting and business produce different versions of the same loss ratio. Without a certified KPI layer and a common data model, committee debate is about numbers, not decisions.
How Datum is structured for insurance.
Datum's architecture extends for insurance with two explicit sector layers: a Common Data Insurance aligned with ACORD and a Business Layer focused on actuarial calculation, Solvency II and IFRS 17. We don't reinvent the architecture — we specialise it.
What it means for an insurer
Core insurance entities (policy, customer, claim, coverage, premium, line of business) are modelled from day one aligned with ACORD. No intermediate translation between a generic CDM and insurance semantics.
Solvency II, IFRS 17, Best Estimate, technical reserves and ORSA are not manually rebuilt every quarter. They are governed, versioned and reconcilable data products.
Each layer has its own ownership: IT owns Landing and Operational, the Data Office governs Common Data Insurance and Actuarial Business, and teams consume from the catalogues.
What we address with Datum — and what we don't.
Data governance enables insurance compliance but does not replace it. We honestly distinguish which frameworks Datum covers as a technical foundation, which we partially align with and which are out of scope.
Covered by Datum
Datum provides the real technical foundation (governance, lineage, quality, ownership, traceability).
Partially aligned
Datum provides the governed data foundation; specific business logic requires other components.
Out of scope
We do not replace actuarial systems or supervision. Let's be clear about this.
ACORD as the semantic map of insurance data.
ACORD (Association for Cooperative Operations Research and Development) is the reference standard for the insurance sector. It defines a common vocabulary of entities and messages recognised globally. We use it as the semantic reference to design Datum's Common Data Insurance.
Why ACORD matters in data governance
It defines standardised entities — Party, Policy, Coverage, Claim, Premium, Loss — recognised by any insurer, broker or reinsurer.
It lets us structure the CDM aligned with how insurance companies actually operate, instead of inventing a proprietary model from scratch.
Speaking ACORD inside an insurer accelerates technical alignment. It avoids weeks of translation between proprietary and standard models.
An ACORD-based assessment or architecture adapts faster to another company. Less reinvention per client, more focus on the real differentiator.
ACORD areas we cover in Datum's CDM
We don't cover the entire standard. We cover the areas that support critical data for underwriting, claims, actuarial and reporting.
Data capabilities designed for the insurance context.
Canonical sector entities: policy, claim, policyholder, insured, coverage, reserve. Common semantics to connect actuarial, accounting, operations and distribution.
Data ownership, actuarial calculation traceability and quality framework aligned with DGSFP and EIOPA requirements.
Automation of load, validation and publication circuits for actuarial and accounting data to reduce close effort and eliminate manual process dependency.
Governed historical data architecture, feature catalogue and quality pipeline as the foundation for auditable and explainable predictive models.
Accelerate Datum in your insurer.
An initial assessment identifies the starting point, pilot domain and deployment model that fit best.