Mastering Negative Screening: Beyond Basic PEP/Sanctions Checks

Excluded from normalized aggregate trends after staff review: 3056 posts were attributed to March 2026; 671 shared March 14, 2026. The preceding six-month median was 13.5 posts.

Review evidence: 3,056 posts in March 2026; 671 shared March 14, 2026; preceding six-month median 13.5. Reviewed August 9, 2026.

This company's pages remain public, but its content is excluded from normalized aggregate trends. Unfiltered raw trends and advanced filtering are available to Accelerate and Lead accounts.

Effective AML negative screening should extend beyond sanctions and politically exposed person lists to include adverse media, criminal records, and high-risk affiliations, creating a fuller risk profile amid evolving financial-crime threats. The approach distinguishes Match Score, which measures confidence that a screened person or entity matches a watchlist record, from Risk Score, which evaluates the severity of risk associated with a credible match using factors such as jurisdiction, category, and record severity. AI-native automation can analyze large volumes of structured and unstructured data, support fuzzy matching and alias detection, reduce manual review, and adapt to emerging risks. Didit presents its modular AML Screening platform as a developer-focused solution that screens against more than 1,300 global watchlists in real time, incorporates adverse-media intelligence and configurable thresholds, and integrates through APIs or a no-code console, with pricing based on successful checks and no setup fees.

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