VP
Joe Doe Don
Sanctions, PEP, wanted
Primary nameJoe Doe Don
Aliases18 variants
Birth date10/07/1952
NationalityRussian Federation
Products / Smart Matching Technology
AML Name Matching Software for Sanctions and PEP Screening
Match names across aliases, spellings, transliterations, and scripts. Checklynx combines identity attributes and sanctions, PEP, wanted, and adverse media records into scored profiles for human review.
Sanctions & PEP (67)Adverse media (3)
Search resultGrouped into 1 standardized profile
Source records clustered into this profileEvidence retained
OFAC SDNSanctionsName + DOB
EU listSanctionsAlias
PEPPolitical exposureRole
WantedWatchlistIdentifier
MLRO view
Likely match. Escalate with source bundle, match score, and risk rationale attached.
One profileMultiple source records clustered together
Match scoringRank profiles by customer alignment
Risk contextSanctions, PEP, wanted, and media signals
MLRO-readyEvidence retained for defensible decisions
Match names across spellings, languages, and scripts
Names rarely appear in one perfect form. Checklynx compares name variants alongside dates, identifiers, nationalities, and source evidence, then groups likely records into a clearer profile before the analyst starts reviewing.
Fuzzy name matching
Retrieve plausible spelling, spacing, punctuation, and name-order variants without treating name similarity as a final identity decision.
Transliteration and scripts
Compare original-script and transliterated names while preserving aliases and alternative renderings for reviewer inspection.
Profile clustering and scoring
Combine names with birth dates, identifiers, nationalities, and source records to rank likely profiles with explainable evidence.
Signal intelligence layer
From fragmented hits to one standardized profile
Smart Matching Technology turns scattered sanctions, PEP, wanted, and adverse media records into a single investigation view. Analysts see the profile, the evidence behind it, and the score that explains why it matters.
Normalize
Names, aliases, dates, identifiers, nationalities, source labels, and entity attributes are standardized before review.
Cluster
Signals from multiple sources are grouped into likely real-world profiles instead of isolated list hits.
Score
Identity fit, corroborating attributes, source support, and conflict signals produce a profile-level match score.
Prioritize
Low-confidence profiles fall away, while higher-risk matches rise to the top for MLRO and analyst review.
Built for MLRO decisions
Fewer false positives. A fuller identity picture.
Incomplete sanctions records can create unnecessary alerts. Checklynx connects identity information across sources to better distinguish similar names. Your team spends less time clearing irrelevant matches and more time reviewing genuine concerns.
MLRO reviewReview priority 1
Recommended disposition: likely match
Name variants, birth date, nationality, and multiple source records support the profile. Sanctions and PEP signals increase the review priority and require documented escalation.
Match score92%
Risk scoreHigh
EvidenceStrong
Give every assessment a clearer candidate profile
Smart matching brings likely name variants, identifiers, attributes, and source records together first. Reviewers can then assess one structured profile instead of reconstructing the evidence from separate hits.
- 1Group
Connect likely variants and records.
- 2Assess
Compare supporting and conflicting evidence.
- 3Decide
Record the reviewer’s outcome and rationale.
Measured impact in screening review
30–40%Less compliance review time
CashDirector reports that clustered profiles and fewer false positives help its compliance officers review cases faster.
20%Less time spent on monthly reviews
Shopware connects retained false-positive decisions, customer screening and ongoing monitoring.
Coverage
Signals that strengthen or weaken match confidence
The score is built from identity similarity, corroborating structured attributes, and source support. Conflicting attributes reduce confidence, while multiple consistent records help the right profile rise.
Identity signals4
Corroboration4
Source supportMulti
Risk contextHigh
| Signal family | Examples | How it helps | Reviewer output |
|---|---|---|---|
| Identity | Names, aliases, associated names, identifiers | Measures whether the screened customer aligns with the profile identity | Profile-level match score |
| Corroboration | Birth date, birth year, nationality, gender | Raises or lowers confidence when structured attributes agree or conflict | Clear evidence and gaps |
| Source support | Multiple linked sanctions, PEP, wanted, and media records | Adds limited confidence when independent records support the same profile | Clustered source bundle |
| Risk context | Sanctions status, PEP exposure, wanted flags, adverse media themes | Separates match likelihood from the risk context attached to it | Risk score and escalation priority |
What is Smart Matching Technology?
Smart Matching Technology is the Checklynx matching layer that groups related screening records into likely real-world profiles, standardizes the profile data, and scores how closely each profile matches the customer being screened.
How does profile-level match scoring help an MLRO?
It helps the MLRO understand whether a result is likely to be a real match, how strong the supporting evidence is, and what risk category is attached to the profile. This reduces manual sorting and makes review decisions easier to evidence.
Does the score replace analyst or MLRO judgment?
No. The score prioritizes and explains the profile, but the final decision remains with the reviewer. Checklynx keeps source records, attributes, and risk signals attached so teams can document their rationale.
Which data points can influence the match score?
The score can use names, aliases, identifiers, associated names, nationality, birth date, birth year, gender, and the number of supporting source records where those data points are available.
How is match score different from risk score?
Match score reflects how strongly the screened customer aligns with a grouped profile. Risk score reflects the seriousness of the signals attached to that profile, such as sanctions, PEP status, wanted status, or adverse media themes.
How does AML name matching software handle fuzzy matches and transliteration?
It retrieves plausible name variants across spelling, punctuation, word order, aliases, scripts, and transliterations, then compares available identifiers and other attributes. These signals help rank candidates, but a reviewer still confirms whether the customer and source profile refer to the same party.