Sanctions screening with far fewer false positives.

The major sanctions regimes: OFAC in the US, Canada, the EU, the UK under OFSI, Australia and APAC jurisdictions, 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. Screening runs on every customer and payment in real time rather than on a delayed or batch basis. Breadth of coverage matters because sanctions exposure is rarely confined to a single jurisdiction alone: a customer or counterparty can be clean against a domestic list while carrying exposure on a foreign one, particularly for cross-border payments or a customer base that spans multiple countries. Because this same list coverage also feeds company screening and payment screening, a party is checked against the same breadth of lists regardless of which screening surface triggered the check.

Spelling variants, homonyms and stale records can turn one true risk into dozens of matches, since a common name can appear across multiple sanctioned individuals or entities, and transliteration differences alone can generate several spellings of what is actually the same listed party. Agentic entity resolution collapses these near-duplicate matches into one scored identity, designed to deliver a significant reduction in false positives without dropping the genuine hit hiding among them. This distinguishes resolution from simple fuzzy matching: fuzzy matching alone tends to produce more candidate matches, not fewer, because it is deliberately permissive about spelling variance, whereas resolution scores identity coherence across those candidates to determine which ones actually represent the same underlying party before a human ever has to adjudicate the difference between a coincidental homonym and a true, if imperfectly spelled, match.

Yes. Each true hit is escalated with a plain-language rationale and source citations, for example an exact name and date-of-birth match against a specific listed entity, rather than a bare confidence score with no stated reasoning behind it. This is deliberate: a reviewer, and later an examiner, needs to understand specifically why a party was escalated, not just that an algorithm assigned it a high score, since a score alone cannot be defended in an examination the way a stated rationale can. The explanation is generated as part of the same entity-resolution process that clears the false positives around it, so the reasoning behind an escalated hit and the reasoning behind a cleared near-duplicate both draw from the same underlying evidence, keeping the two decisions consistent with each other rather than the product of two disconnected processes.