A false positive in sanctions screening is a hit where a customer's name resembles a sanctioned or politically exposed person without actually being a match. Reducing them means improving matching logic, source data quality, and review workflow — not lowering how sensitively you screen, which raises the risk of missing a genuine match.
What causes false positives in sanctions screening?
Most false positives come from three sources: common names that overlap with entries on a sanctions or PEP list, transliteration differences between how a name is spelled in different scripts or documents, and matching logic that's tuned too broadly for the data it's screening against. A screening tool with weak fuzzy matching will flag far more look-alikes than one with algorithms designed for name variation.
How do matching thresholds affect false positive rates?
Matching thresholds control how close a name has to be to a watchlist entry before it's flagged. Set too loose, thresholds surface every minor spelling variation as a hit; set too tight, they risk missing a genuine match written slightly differently. The right threshold is rarely one-size-fits-all — it should reflect the risk profile of the customer base being screened, with tighter tolerances for higher-risk segments.
What role does data quality play?
Screening is only as accurate as the data behind it. A well-maintained sanctions and PEP database — one that's deduplicated, correctly categorised, and updated as lists change — produces fewer spurious matches than a stale or poorly structured one. This is also why database refresh frequency matters: a list that's a week out of date can both miss new listings and carry outdated entries that no longer apply.
How should due diligence workflows handle a false positive?
Once a hit is confirmed as a false positive, it should be recorded as a documented due diligence decision, not just dismissed. Whitelisting that decision means the same non-match won't be re-flagged on every future scan of that customer, which is where a lot of unnecessary repeat review work comes from.
What does "good" look like for a sanctions screening programme?
A mature programme combines configurable matching thresholds, fuzzy and phonetic matching, a regularly refreshed data source, and a documented whitelisting process — reviewed periodically rather than set once and left alone. PEP and sanctions screening built this way keeps genuine risk visible while cutting the noise around it, and pairs naturally with an AML risk assessment that routes higher-risk matches to deeper review.
