Presentation: Real-Time Data Analysis and ML for FraudPrevention

Track: Next Gen Banking: It’s not all Blockchains and ICOs

Location: Fleming, 3rd flr.

Duration: 4:10pm - 5:00pm

Day of week: Tuesday

Level: Advanced

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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.

Speaker: Mikhail Kourjanski

PayPal Lead Data Architect, Risk and Compliance Management Platform

Mikhail Kourjanski is the Lead Data Architect at PayPal, responsible for the data architecture of the PayPal real-time decisioning platform that handles billions of events per day and maintains dozens of petabytes of data. For fraud prevention function alone, this platform saves more than $500M in annual profits.
Mikhail has over 20 years of work experience, including high-tech software engineering, academic research, and consulting for the Financial Services industry. Mikhail’s architecture work includes a number of innovative developments such as high-performance distributed processing over eventually consistent data, multi-layer security model for data-in-transit middleware, service domain models for banking and Fintech clients. Mikhail had delivered multiple engagements for the Top-10 banks in the roles of trusted advisor up to CIO level, lead architect, and IT delivery executive. Prior to consulting period of Mikhail’s career, he proved a successful entrepreneur running his own company, winning and delivering R&D projects for the US Government agencies. Mikhail earned his Ph.D. degree in applied mathematics from the Moscow State (Lomonosov) University, Russia, followed by the post-doctoral research position at UC Berkeley. Mikhail’s academic research focused on large-scale distributed systems and real-time simulations for the Transportation industry and Smart Cars technologies.

Find Mikhail Kourjanski at

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