What is AML screening?

AML screening is the process of comparing a customer, business, or counterparty against sanctions, politically exposed person, and other risk watchlists, then routing possible matches into a documented review instead of treating every fuzzy name hit as a final legal decision.

What teams actually screen

Product and compliance teams typically screen individuals and legal entities at onboarding, then again when a payment counterparty appears or when a watchlist is updated. The useful output is not a yes-or-no label. It is a structured match status, identifiers to keep, and a path to clear, review, or escalate.

How Naiza implements AML screening

Naiza exposes an AML screening API so a server can submit a name with customer or counterparty context, receive classified results from multiple list sources, and attach the screening identifier to later events. A possible match is a risk signal for review. Naiza does not replace your compliance policy or issue a legal determination.

When this page is not enough

If you need endpoint fields, polling, or webhook handling, use the AML product page and the AML integration guide. This glossary page stays on the definition and the decision workflow so search and answer engines can cite a short, accurate explanation.

Frequently asked questions

Short answers written so search and answer engines can cite them.

What is an AML screening API?

An AML screening API lets your server submit a person or entity and receive structured watchlist results that your product can clear automatically or send to a reviewer.

Does a watchlist match mean you must block the customer?

No. A possible match is a signal to review supporting data against your policy. False positives from similar names are common, and the final decision belongs to your compliance process.

Which lists does AML screening usually include?

Common sources include sanctions lists such as OFAC, UN, and EU lists, plus PEP and other watchlist datasets. Coverage should be stated on the product page rather than inferred.