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Name Matching Isn’t a Silver Bullet

Human factors drove TD Bank’s OFAC violations

OFAC recently fined TD Bank, N.A. for maintaining accounts for four years owned by someone on its watchlist of Specially Designated Nationals and Blocked Persons (the “SDN List”). The bank failed to match “Esperanza Maradiaga Lopez” against the OFAC SDN List entry of “Esperanza Caridad Maradiaga Lopez,” despite receiving sanctions screening alerts on four occasions over a four-year period.

Once the errors were discovered, TD Bank voluntarily identified, reported, and addressed the issues. Still, this failure from a large, global financial institution is a wake up call for all organizations dealing in high-volume screening — technology is not a silver bullet. Companies must ensure they have well-practiced procedures in place to properly handle alerts.

To mitigate the failures that triggered the violations, TD Bank1:

  1. Implemented an explicit sequence for adjudicating and escalating alerts
  2. Implemented more specific standards and examples for determining a name match, accounting for spelling variations or abbreviated, transposed, or middle names
  3. Implemented more specific standards and examples for determining a date-of-birth match
  4. Required additional training related to the root causes of these violations.

Scaling Issues in Financial Compliance

As governments increasingly scrutinize onboarding practices and more heavily regulate ongoing screening, compliance teams are tuning and scaling their systems and processes to support analyst teams that are already overwhelmed by huge volumes of false positives. Scaling requires organizations to add more people to analyst teams. However, they have difficulty finding analysts with the proper skills. This need to continually add analysts grows as the company grows. However, according to a Thomson Reuters survey, 65% of companies say their compliance staffing will remain the same over the next year. To reduce costs, organizations look to multitiered screening teams, with initial reviews by less-skilled analysts, which puts them at risk of attracting even more attention from government oversight.

Root Causes

Adding people helps, but it doesn’t address the processes needed for training new hires and equipping them with proper escalation procedures. It also doesn’t address the root causes of humans limited by speed, distraction, and fatigue, which greatly contribute to true positives being missed in the sea of false positives.

How Technology Can Help

While technology cannot solve the problem of poorly trained staff ignoring true alerts, it can compensate for human limitations by reducing the number of false alerts they must review. Specialized name matching with machine learning like Rosette® can be customized to an organization’s screening and risk profiles to reduce false positives by as much as 90%. It can:

  • Handle nicknames, incomplete or transposed names, formatting issues including missed spaces and capitalization, and challenges such as transliteration from languages that use a non-Latin script
  • Fuzzy match not only names, but also birthdates, and addresses
  • Connect third-party data with in-house screening data.

Technology cannot eliminate false positives or the risk of mistakes. But when incorporated with internal resources — a team of skilled analysts and a set of comprehensive processes — it will make your AML/KYC programs more effective and efficient.

Interested in reducing false positives by up to 90% without having to rip and replace your existing screening systems? See how with Rosette Name Matching in this 3-minute video or contact us for a customized demo.

End Notes

1. Department of Treasury, Enforcement Release, 12/23/2021

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