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Presentations
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Generative AI: Shaping a New Future for Fraud Prevention
Neha Narkhede discusses a vision for fraud and risk management that leverages the advancements in generative AI.
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Applying Machine Learning to Financial Payments
Tamsin Crossland discusses how ML can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
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High Performance Cooperative Distributed Systems in Adtech
Stan Rosenberg explores a set of core building blocks exhibited by Adtech platforms and applies them towards building a fraud detection platform.
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ML Data Pipelines for Real-Time Fraud Prevention @PayPal
Mikhail Kourjanski focuses on the architectural approach towards PayPal’s real-time service platform that leverages ML models, delivers performance and quality of decisions.
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Real-Time Data Analysis and ML for Fraud Prevention
Mikhail Kourjanski addresses the architectural approach towards the PayPal internally built real-time service platform, which delivers performance and quality of decisions.
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Data Pipelines for Real-Time Fraud Prevention at Scale
Mikhail Kourjanski discusses the architecture of PayPal’s data service which combines a Big Data approach with providing data in real time for decision making in fraud detection.
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Counterfactual Evaluation of Machine Learning Models
Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.
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AI in Finance: from Hype to Marketing and Cybersec Applications
Natalino Busa illustrates a number of use cases of using AI and machine learning techniques in finance, such as transaction fraud prevention and credit authorization.
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Fighting Online Fraud and Abuse with Large-Scale Machine Learning at Sift Science
Jacob Burnim discusses Sift’s approach to building a ML system to detect fraud and abuse, including training models, handling imbalanced classes, sharing learning, measuring performance, etc..
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Solving Payment Fraud and User Security with ML
Soups Ranjan talks about Coinbase’s risk program that relies on machine learning (supervised and unsupervised), rules-based systems as well as highly-skilled human fraud fighters.
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Large Scale Machine Learning for Payment Fraud Prevention
Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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