Presentation: Optimizing Fraud Model Thresholds @Airbnb

Track: ML in Action

Location: Cyril Magnin III

Duration: 4:00pm - 4:10pm

Day of week: Tuesday

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Abstract

Like all online businesses, Airbnb faces fraudsters who attempt to use stolen credit cards. In this talk I’ll walk through how we leverage machine learning, experimentation, and analytics to identify and block fraudsters while minimizing impact on the overwhelming majority of good users.

First, I’ll introduce how we use machine-learning models to trigger frictions targeted at blocking fraudsters. Then, I’ll outline how we choose the model’s threshold by minimizing a loss function, and dive into each term in the loss function: the costs of false positives, false negatives, and true positives. Finally, I’ll walk through a numerical example comparing the optimization of blocking transactions versus applying a friction.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Dave Press

Data Science Manager @Airbnb

Dave is a data scientist working on Trust and Risk at Airbnb. He focusses most on financial fraud and offline risk. Prior to joining Airbnb he worked in healthcare and hardware.

Find Dave Press at

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.