# Fraud detection on GCP

Throughout this tutorial, we’ll walk through the creation of a production-ready fraud prediction system. A prediction is made in real-time as the user makes the transaction, so we need to be able to generate a prediction at low latency.

## [Fraud Detection Example](https://github.com/feast-dev/feast-fraud-tutorial)

Our end-to-end example will perform the following workflows:

* Computing and backfilling feature data from raw data
* Building point-in-time correct training datasets from feature data and training a model
* Making online predictions from feature data

Here's a high-level picture of our system architecture on Google Cloud Platform (GCP):

![](https://4022989944-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FisIrpWtTx8fs6njvYvXY%2Fuploads%2Fgit-blob-f5a0dd450b6e71634d960ce2af36d14f435ff8ef%2Fdata-systems-fraud-2x.jpg?alt=media)

| ![](https://4022989944-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FisIrpWtTx8fs6njvYvXY%2Fuploads%2Fgit-blob-880d31b4955de605aeec9ec855359f6858fbf3b1%2Fcolab_logo_32px.png?alt=media) [Run in Google Colab](https://colab.research.google.com/github/feast-dev/feast-fraud-tutorial/blob/master/notebooks/Fraud_Detection_Tutorial.ipynb) | ![](https://4022989944-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FisIrpWtTx8fs6njvYvXY%2Fuploads%2Fgit-blob-8b25551a97921681334176ee143b41510a117d86%2Fgithub-mark-32px.png?alt=media)[ View Source on Github](https://github.com/feast-dev/feast-fraud-tutorial/blob/main/notebooks/Fraud_Detection_Tutorial.ipynb) |
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