The Problem of Name Matching

When it comes to Anti-Money Laundering (AML) and Sanctions screening, the complexities of name matching represent a significant obstacle for compliance professionals. Although fuzzy matching algorithms can help navigate challenges such as typos or incomplete strings, they fall short when dealing with nuances like transliterated names, variations of nicknames, or missing name elements. As a result, companies often resort to broad, imprecise search parameters. This approach, unfortunately, leads to an overwhelming number of false positives, and more critically, the risk of false negatives.

Overcoming Name Matching Challenges with sanctions.io

At sanctions.io, we have developed a next-generation name-matching solution that merges machine learning with traditional methods like name lists, common keys, and rule-based systems. This fusion allows us to generate a comprehensive matching score, effectively overcoming the unique challenges posed by name matching in AML and Sanctions compliance.

By applying this advanced technology, we provide our customers with a robust tool to enhance their compliance processes, reducing the risk of false positives and negatives.