Hundreds Of Alerts, One Real Match
Screening customers against sanctions and watchlists means matching names written in many ways: transliterations, nicknames, initials, missing middle names, company suffixes. Loose matching floods analysts with false positives. Tight matching misses the variant that matters.
Two false alerts on common names, and the transliterated match is never raised.
Name Screening Is Both Noisy And Leaky
Screening engines compare names, and names are a poor key. Common names produce matches against unrelated listed persons. Transliteration from Arabic, Cyrillic or Chinese produces many valid spellings of the same person. Companies add or drop suffixes and translate their names.
Teams tune thresholds to manage the volume, and every tuning is a trade-off: lower the threshold and analysts drown in alerts, raise it and a real match slips through. The same customer is often screened several times, once per product record, each producing its own alerts.
Analysts then spend most of their time clearing alerts that a person would dismiss at a glance, using date of birth, nationality or address that the name match ignored.
Floods Of False Positives
Common names match unrelated list entries, and every one must be cleared by hand.
Missed Variants
Transliterations and reordered names slip past tuned thresholds.
Duplicate Screening
The same customer is screened once per record, producing repeated alerts.
Resolve The Customer, Then Screen
Resolve each customer’s records into one entity first, combining every spelling, document name and attribute you hold. Then screen that entity once, with the full set of names and supporting details available for comparison.
Use matching that understands name variation instead of string distance alone, and that weighs other attributes such as date of birth and country. Alerts drop to the candidates where the evidence actually agrees.
The customer’s variants are resolved first, so one consolidated record is compared with the list.
Screen Each Customer Once
One resolved entity per customer, carrying every name variant you hold.
Use More Than The Name
Dates of birth, nationality and addresses support or rule out a name match.
Clear Alerts Faster
Analysts see the evidence for each candidate side by side.
The Identity Layer Under Financial Crime
Zingg runs as a workload inside your data platform and gives every entity one persistent ZINGG_ID. Everything above it reads the same resolved entities.
The same layer powersCustomer Analytics & MarketingPrivacy & ComplianceSupply Chain & ProcurementProduct 360Master Data ManagementAI & Data Foundations
Matching Built For Names That Vary
Nicknames And Dictionaries
Nicknames, transliterations and company-name dictionaries are built in. How Zingg matches
Trained On Your Labels
Zingg learns which differences matter from pairs your analysts label, instead of one fixed threshold.
Explainable Matches
Every candidate shows which fields matched and how closely. Explainability
Test Changes Safely
Compare match results before and after a model change with Model Diff.
Sanctions Screening Questions
How does Zingg reduce screening false positives?
It resolves each customer into one entity with all their name variants and attributes, and matches with a model trained on your labels, so alerts are raised where names and supporting details agree.
Does Zingg replace my screening engine?
No. Zingg resolves customer identities and can match records against lists you hold in your data platform. Screening decisions and case handling stay with your existing tools and teams.
Can Zingg handle transliterated names?
Yes. Learned matching, nickname handling and dictionaries cover common spelling and transliteration variants.
Can we check the effect of a model change before using it?
Yes. Model Diff compares results from two configurations and shows every record whose match changed.
Review Real Candidates
Resolve customers before you screen, in your own data platform.