The same person is Müller on one list, Mueller on another and Muller on a third. Character-by-character comparison misses all three. Krino flattens the differences first, then compares.
Characters like é ü ø ß æ ı ğ ş are reduced to their base forms. Cases that standard decomposition does not solve — Turkish dotless ı among them — are handled explicitly.
Given-name order, middle names, titles and punctuation differences do not break a match. The comparison works over name parts.
Close-but-not-identical spellings are scored by similarity. You set the threshold — you are the only party who knows how many false alarms you can absorb.
Consolidated lists from the major sanctions regimes are gathered into one source and refreshed regularly. People and organisations go through the same screening.
People who require heightened scrutiny because of public office are flagged as their own category. A PEP match is usually a reason to look, not a reason to decline — Krino lets you keep the two apart.
Internal deny lists, people you have previously caught, or a list shared by a partner all screen through the same mechanism.
Lists change; a customer who was clear yesterday may be listed today. Krino re-screens your existing customer base at an interval you choose and opens cases for new matches.
A name matching does not automatically decline the transaction. The match enters your rules as a signal; what happens next is up to you.
Which list, which record and at what similarity — all recorded.
A sanctions match declines outright, a PEP match opens a review — as you configured it.
Anything needing review lands in the team's queue with the matched record inside the file.
False match or real — who decided, when and how, in the audit trail.
How many matches does today's customer list produce, and how many are false alarms? Let's find out together.