Valley Bank Reduces Anti-Money Laundering False Positive Alerts by 22%
Company Size
1,000+
Region
- America
Country
- United States
Product
- DataRobot AI Cloud
- MLOps
- AutoML
Tech Stack
- Predictive Analytics
- Automated Modeling
- AI Lifecycle Optimization
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
- Predictive Quality Analytics
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Valley National Bank is the principal subsidiary of Valley National Bancorp. It is a regional bank with approximately $50 billion in assets. The bank operates many convenient branch locations and commercial banking offices across New Jersey, New York, Florida, Alabama, California, and Illinois. Valley is committed to providing the most convenient service, the latest innovations, and an experienced and knowledgeable team dedicated to meeting customer needs. The bank is also committed to helping communities grow and prosper, which is at the heart of Valley’s corporate citizenship philosophy.
The Challenge
Valley Bank, a regional bank with approximately $50 billion in assets, was facing a challenge in its Anti-Money Laundering (AML) department. The bank was dealing with an overwhelming volume of false positives in its effort to uncover money laundering activities across millions of transactions. The bank's AML team was seeking to reduce the manual work involved in predictive modeling. The process of creating models manually was time-consuming, taking weeks to complete. The bank was looking for a solution that could automate its fraud detection process and manage the volume of false positives in a realistic way.
The Solution
Valley Bank turned to DataRobot AI Cloud to automate its fraud detection process. The AI Cloud optimizes the entire AI lifecycle, helping to manage the volume of false positives without the need for data scientists on staff. The bank's model risk management team was involved from the start to validate the results. The bank fed predictions into its AML case management system and, alongside DataRobot data scientists, built and validated more than 100 models with a robust backtesting strategy. The bank also generated 175 features via DataRobot's automated Feature Discovery. During the trial, the bank reduced false positives by more than 30 percent. The bank's model risk management team was able to recreate those models successfully in the platform.
Operational Impact
Quantitative Benefit
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