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Financial Crime

Levenshtein, Jaro-Winkler, and the Art of Finding the Name You Almost Missed

Not all fuzzy matching algorithms are equal, and in sanctions screening, the difference matters. This article breaks down how Levenshtein and Jaro-Winkler handle real name variations, why your similarity threshold is a compliance decision, and what a robust matching strategy actually looks like. Essential reading for compliance officers, risk teams, and RegTech professionals.

Financial Crime

Your AML Programme is only as Good as your Worst Algorithm

Your AML programme is only as strong as its weakest algorithm. Many compliance teams rely on vendor metrics like accuracy without understanding the underlying model performance. This creates a critical risk gap in AI governance. This blog explains why the confusion matrix is essential for interpreting AML models, how weak algorithms impact the entire compliance chain, and the key questions teams should be asking vendors. With regulators like the Financial Conduct Authority, Financial Crimes Enforcement Network, and Monetary Authority of Singapore increasing scrutiny on AI, understanding model performance is now a core compliance responsibility.

Financial Crime

The EU Digital Omnibus on AI A Double-Edged Sword for Financial Crime Compliance

Compliance Training Is Ready For Its Moment

Aithea is rethinking compliance education from the ground up. Learn how NanoAcademy's microlearning approach is helping trade, cargo, and financial crime professionals build knowledge that actually sticks.

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