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

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • 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

  • Real-world Data Engineering

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