Cloud money laundering8/3/2023 When looking for ways to automate its fraud detection, Valley Bank found most solutions primarily offered segmentation and were difficult for non-data scientists to use. “When so many are false positives, you run the risk of missing the items that provide actual value.” “Our team has to work through a lot of noise to find productive information, which results in staffing challenges and a challenge in keeping staff engaged,” explained Jennifer Yager, Director of Financial Crimes Compliance. At Valley Bank, predictive analytics helped comb through millions of transactions across three-quarters of a million customers, but it took weeks to create models manually. DataRobot Success Stories See how organizations like yours have realized more value from their AI initiatives.Ĭutting Throught the Volume of False Positivesįor financial institutions, uncovering anti-money laundering (AML) crimes can be an exhaustive process, and all the more so when 95 percent, on average, are false positives.Deployment Infrastructure Choose how you want to deploy DataRobot, from managed SaaS, to private or public cloud.Platform Integrations Unify your data warehouses, ML APIs, workflow tooling, BI tools and business apps.Monitor and Measure ROI Monitor, measure and diagnose model accuracy, ROI, and bias in real-time from any hosting environment.Integrate Models Deploy and integrate any model, anywhere with multiple deployment options.Validate and Govern Models Create a centralized system of record for all models, test, approve, and automate compliance documentation.
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