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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.

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

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks