Presentation: Real-Time Data Analysis and ML for FraudPrevention
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Abstract
PayPal processes about a billion dollars of payment volume daily ($354bn in FY2016); complex decisions are made for each transaction or user action, to manage risk and compliance, while also ensuring good user experience. PayPal users can make payments immediately in 200 countries with the assurance that the company’s transactions are secure. In today’s world, as fraud becomes more and more sophisticated, how does PayPal achieve this goal? While many industry solutions rely on fast analytics performed in near-real time over streaming data, our business requirements demand real-time, millisecond-range response. This talk will address the architectural approach towards our internally built real-time service platform, which delivers unparalleled performance and quality of decisions. This platform blurs the line between Big Data and sustainable support for a high volume of real-time decision requests. Hence, we will have a look at the Machine Learning risk models lifecycle and deployment in production for inference at scale, and outline key challenges and lessons learned for deployment on real-time event-driven, fast data in-memory architecture that accelerates detection and decisions, thereby reducing losses, improving customer experience, and allowing efficient new integrations.
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