Presentation: Data Pipelines for Real-Time Fraud Prevention at Scale

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 2:45pm - 3:35pm

Day of week: Tuesday

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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. 

How does PayPal achieve this goal in today's complex environment filled with "high-level" fraudsters as well as constantly increasing customer demand? 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. Well-structured design, along with domain modeling methodology provide for high adaptability to emerging fraud patterns and behavioral variations, 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

Lead Data Architect @Paypal

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.

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.