Anti-Money Laundering (AML)
Records on sanctions and watch lists for AML are relatively small in number, but the consequences for missing a match can be severe. Every organization wants to use the most accurate matching engine to prevent such misses while minimizing false positives, rather than being complacent about what has been done so far. The challenges of name matching, especially matching against databases of names from various cultures and ethnicities, are enormous. Traditional techniques are far from adequate. Machine learning-based NetOwl NameMatcher brings AI to AML so that organizations can put their very best efforts into preventing money laundering and other illegal activities.