Risk intelligence engineered for screening decisions.

Sanctions data spanning OFAC, Canada, the EU, the UK, Australia and APAC regimes, including comprehensively sanctioned geographies, vessels, ports and restricted securities, plus trade restriction lists such as the US BIS lists, the World Bank Listing of Ineligible Firms and Individuals, and the Canada Export Controls List. Alongside that sit tiered PEP data covering relatives, close associates and heads of international organizations, relevant adverse media with source citations attached to every finding, and linked entity data across aliases and connected parties, including deep-tier ownership structures affected by the 50% Rule. The reason these live together as one data layer, rather than separate feeds a screening tool has to reconcile itself, is that false positives and missed risk both tend to originate in bad or disconnected data upstream of the actual matching logic. This data structure is what our screening system consumes, and it is also available on coverage-based subscriptions as the risk-intelligence layer behind an existing screening stack.

The 50% Rule is the sanctions-attribution principle that an entity majority owned or controlled by a sanctioned party is itself treated as sanctioned, even when that entity is not separately named on any list. It exists precisely because sanctioned parties can and do route activity through subsidiaries, holding structures and nominee ownership that never appear on a watchlist directly, so a name-only screen against the parent list alone would miss the exposure entirely. Our data layer maps deep-tier ownership and close associate networks so that this kind of exposure surfaces even when the sanctioned party is several ownership layers removed from the entity actually being screened. This is structured as part of the underlying data layer rather than a one-off check, so it applies consistently across customer screening, company screening and ongoing monitoring, rather than requiring a separate manual lookup whenever a reviewer happens to suspect a corporate structure is worth tracing.

Records are continuously maintained and refreshed on a regular cadence across sanctions, trade restriction, PEP and adverse-media sources, so screening reflects the current risk picture rather than a stale view of the world. Freshness matters more than it might first appear: sanctions lists change on regulator timelines that do not wait for a vendor batch schedule, and a customer cleared last week against a list that has since been updated is effectively being screened against outdated information until the next refresh catches up. The same principle applies to adverse media, where relevant negative news can appear at any time, and to PEP status, which changes as individuals move in and out of politically exposed roles. Because ongoing monitoring re-screens the customer book against this same maintained data, freshness is not just a property of the initial screen, it is kept up for as long as the customer relationship exists.

Stale, thin or unstructured records are what generate false positives and missed risk downstream, more often than the matching logic itself. A record with no identifiers beyond a name forces any screening engine, however well designed, to match on name alone, which is exactly the pattern that produces homonym-driven false positives and, in the other direction, missed true matches hiding behind a spelling variant. BriteBase data is structured specifically for agentic entity resolution, carrying the relationships, roles and context that let the resolution engine tell a genuine match from a coincidental one, and carries the context that makes each surviving hit explainable rather than a bare score. In practice this means the false-positive reduction claimed by BriteBase screening products is not solely a function of clever matching algorithms; it depends on the underlying data being rich and current enough for that matching logic to have something real to work with.