Most insurance firms have a Data Control Framework. Most would not withstand a serious audit. The Provenance Method gives you genuine, auditable assurance over your critical data flows — not the appearance of it.
A practical guide to building data control frameworks that perform under regulatory and audit scrutiny. From flow mapping to residual risk — the full methodology.
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The failure is rarely a shortage of controls. It is a failure of precise risk articulation — and it is baked in at the design stage. Three recurring reasons explain why experienced teams with well-maintained frameworks still get caught out.
Risk statements written by governance teams rather than subject matter experts result in language that fails to describe risk at a level that is actionable. Vague risks produce generic controls. Generic controls produce the illusion of assurance.
Precise risk articulation creates accountability. Vagueness — consciously or not — provides cover. Frameworks built from the inside rarely surface the exposures that matter most, because surfacing them is uncomfortable.
Many frameworks are built around the controls that already exist rather than the risks that actually matter. The result looks complete on paper but leaves material exposures unaddressed — in precisely the places that get you in front of a regulator.
A control framework that tells you what you already wanted to hear is not providing assurance.
It is providing reassurance — and reassurance is considerably less useful when something goes wrong.
The correct starting point is not the risk register. It is the data — specifically, a precise map of how data is created and actually moves through your organisation.
Once the data flow is accurately mapped, inherent risks become visible in a way they cannot be from a high-level process description. Precision here is not optional.
When a risk is articulated precisely, the appropriate control often becomes self-evident. It follows logically from an honest description of what could go wrong.
Responsible for the quality and integrity of critical data, but working with frameworks that were built before the current regulatory environment. The Provenance Method gives you a defensible, auditable foundation.
Facing regulatory reviews where the gap between a control that exists and a control that works becomes acutely visible. You need a residual risk position you can actually defend — not one that reflects optimism.
Whose outputs — capital models, regulatory returns, financial statements — are only as reliable as the data flowing into them. A framework built on the Provenance Method gives you traceability from use case back to source.
Charged with providing independent assurance over data controls — but finding that the frameworks you are asked to assess were not built with auditability in mind. We design frameworks that give second and third line something real to work with.
Provenance Data Risk Partners was founded by Navin Ahuja, a specialist in data quality assurance for the insurance sector with direct experience designing and auditing data control frameworks across major insurance firms.
The Provenance Method was developed from that experience — from the recurring gap between frameworks that looked adequate and frameworks that actually held up when tested.
"The most dangerous position is not knowing your framework is inadequate. It is believing it is adequate — and finding out otherwise during a regulatory review."
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