Case Study: Valley Bank achieves 22% reduction in AML false positives with DataRobot

Valley Bank Reduces Anti-Money Laundering False Positive Alerts by 22%

Valley Bank faced an uphill battle with anti-money laundering (AML) monitoring: manual predictive modeling across millions of transactions created high false-positive rates and long model development cycles. To address this, Valley Bank engaged DataRobot, using its AI Foundation, Predictive AI and AI Governance capabilities to simplify modeling and reduce the manual workload for a lean AML team.

DataRobot delivered an automated, end-to-end AI platform that integrated with the bank’s case management, built and validated 100+ models (generating 175 features), and onboarded users in two weeks so the bank could operate without a dedicated data scientist. As a result, Valley Bank now creates or retrains models in a day instead of weeks, reduced total alert volume by about 22% monthly (trial showed >30% false-positive reduction), and increased alerts escalating to case by three percentage points thanks to the DataRobot solution.

Valley Bank

Jennifer Yager

Director of Financial Crimes Compliance