Sanctions and PEP List Verification with AI

A situation where a verification system generates an alert for an entity that isn’t actually sanctioned – even though its name or data resembles a sanctions-list entry. A common example is a popular surname that appears multiple times in sanctions databases for different people. The more verifications you run daily, the more of these alerts pile up, and every one needs manual review. Vercly’s Sanctions AI module reduces this by delivering a context-based conclusion, not just a string-similarity score.

Classic fuzzy matching only compares text strings – say, an 87% match score. AI goes further: it combines the verified entity’s profile (country, date of birth, business type, relationships) with the full context of the sanctions-list entry (aliases, dates, description). If a 90% name similarity is explained away by other data (a different date of birth, a different country), the module auto-classifies it as a false positive without involving an analyst. The decision stays with the human, but the work is far easier.

Standard verification compares client data against sanctions lists via matching algorithms and generates alerts above a set threshold. Sanctions AI adds a contextual layer: pulling entity data from 110+ sources, matching it against detailed sanctions-list descriptions, then running a language model that assesses semantic similarity and issues a reasoned conclusion – with a ready-made PDF report and audit trail.

Yes. Available in an on-premise variant (installed on the client’s infrastructure) and as a dedicated EEA-based cloud instance. Both meet DORA’s requirements around data sovereignty, ICT supply-chain auditability, and operational resilience, with full technical documentation and a DORA-compliant SLA.

No – and it shouldn’t. Sanctions AI supports the analyst’s work; it doesn’t replace the human. Final decisions and responsibility for classifying a hit always rest with the analyst and the organization. AI shortens decision time – reasoned conclusions, aggregated data, ruling out obvious false positives – but final sign-off belongs to a human, as regulators require.

80+ sanctions lists across 27 markets and 50 U.S. jurisdictions (OFAC SDN, OFAC Non-SDN, EU Consolidated List, UK OFSI, UN lists, Poland’s MSWiA list and others). Lists update in real time; the version used is recorded in the audit trail.

Yes. Vercly offers an on-premise option where the language model (LLM or SLM) installs directly on the client’s infrastructure. Verified-entity data never leaves the organization’s IT environment – key for banks and large financial institutions under strict DORA/AI Act/internal-policy requirements.

A tamper-proof, chronological record of every verification operation – queries, alerts, analyst decisions, closed hits. Under Polish and EU AML law and supervisory requirements (KNF, GIIF), an obliged entity must prove not just the result but how it got there. A missing audit trail is treated by inspectors as equivalent to no verification at all.

Cost depends on volume, deployment model (SaaS/on-premise), and integration scope. Book a free demo with our team for a quote tailored to your volume and requirements.

Yes. Alongside Sanctions AI, Vercly offers a PEP AI module using the same contextual approach for politically exposed persons – 700,000+ worldwide, ~30,000 Polish PEP records, >97% date-of-birth coverage, all 215 statutory PEP categories, combined with external sources for fast, context-aware identification of PEPs and their closely associated persons (RCA).