Fraud detection without labeled data

Key takeaways
  • Machine learning is not a silver bullet.
  • Unsupervised anomaly detection techniques work well.
  • How the world has changed with the advent of real-time streaming data.
  • How to leverage new deep learning techniques without a lot of data.

Fraud is a big deal -- it costs the economy several billion dollars a year. For many years, machine learning has been the tool of choice for solving fraud. Unfortunately, machine learning is only useful when you have lots of high-quality labeled data. In the real world it's difficult, expensive, or impossible to get this high-quality labeled data.

In my talk I'm going to look at the problem of fraud at a high level, how it can be solved using unsupervised techniques that don't require labels, and the infrastructure required to implement it.

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