Presentation: Detecting Similar Id Documents Using Deep Learning

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 2:20pm - 2:30pm

Day of week: Wednesday

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Abstract

Identity verification is the process in which we digitally confirm the legitimacy of a real world document. This process is necessary for many businesses to meet their compliance requirements and mitigate their fraud risk. A common form of fraud is the duplication and alteration of stolen documents across multiple user accounts. In this talk, we will discuss how Coinbase solves the problem of detecting similar identity documents using deep learning.

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: Burkay Gur

Risk Engineer @Coinbase

Find Burkay Gur 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.