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Case Study: Valley Bank reduces anti-money-laundering alert volume 22% with DataRobot

Valley Bank Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Valley Bank
Industry
Financial Services / Banking
Challenge
Cut manual AML modeling work and overwhelming false-positive rates.
Headline result
22% reduction in total AML alert volume and a 3-percentage-point increase in alerts escalating to cases

Key results

22%
reduction in total alert volume
monthly
3 pts
increase in alerts escalating to cases

The challenge

Valley Bank's anti-money-laundering team needed to reduce manual work in predictive modeling while managing high false-positive rates across millions of transactions, and wanted to do so without hiring dedicated data scientists.

The solution

Valley Bank adopted the DataRobot AI Platform to automate model building and deployment for its financial-crimes and AML compliance workflows, making modeling accessible to its existing team.

The DataRobot AI platform is so straightforward, I don't have to hire a data scientist to use it.

CP
Chris Phillips
Director of AML Compliance, Valley Bank

The results, in context

Valley Bank reduced total monthly alert volume by 22% while increasing the share of alerts escalating to cases by three percentage points — surfacing more genuinely suspicious activity while cutting noise.

Products used

DataRobot DataRobot AI PlatformDataRobot Predictive AI