Namesakes on the List: Composition Amplification and Target-Decoy Calibration in Sanctions Name Screening

Sanctions screening compares each customer's name with every entry on a watchlist of tens of thousands of listed persons and their aliases, and raises an alert when any entry scores above a similarity threshold. We show that this one-to-many design, more than the pairwise matcher, decides who bears the false alerts. Screening non-sanctioned politically exposed persons from 36 countries against a consolidated list of about 40,000 sanctioned persons, a standard fuzzy matcher at a common operating threshold alerts on people from the countries most represented on the list more than an order of magnitude more often than on others. We formalize this composition amplification: a customer's false-alert probability grows with the number of listed namesakes, so no single score threshold can equalize alert burden across populations whose names collide with the list at different rates. We then adapt target-decoy competition from proteomics: each customer is screened against the real list and against decoy lists of non-sanctioned persons drawn to match the list's composition, and is flagged only when the real list wins. When listed and decoy names are exchangeable, the resulting per-customer p-value bounds every customer's false-alert probability, and hence every group's, without using the customer's demographic attributes. On real lists the guarantee is only as good as the decoys: we show how composition misspecification shifts false alerts between groups rather than removing them, and quantify the recall the calibration costs for listed persons with common names. We evaluate string, embedding and large-language-model matchers and release the benchmark.