# 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://1761717571-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LqPPgcuCulk4PnaI4Ob%2F-Mj5ZuHtKuRgSZbTbDVK%2F-Mj5_6HARkvzXxeiCyPG%2Fdata-systems-fraud%402x.jpg?alt=media\&token=50faba21-0df4-444e-971a-11fd42af019e)

| ![](https://1761717571-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LqPPgcuCulk4PnaI4Ob%2F-MfiN7SjAdK8EOKheOJG%2F-MfZcvLnb8TycZ9lpI4x%2Fcolab_logo_32px.png?alt=media\&token=dc63b24d-e6d4-4dd5-83c9-f2666e70bf6b) [Run in Google Colab](https://colab.research.google.com/github/feast-dev/feast-fraud-tutorial/blob/master/notebooks/Fraud_Detection_Tutorial.ipynb) | ![](https://1761717571-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LqPPgcuCulk4PnaI4Ob%2F-Mf_U72FGoPIONZueIr9%2F-MfZd9RB0PHVz5E171mk%2FGitHub-Mark-32px.png?alt=media\&token=328a28de-40b8-441d-8da8-790d3fb4d5c8)[ View Source on Github](https://github.com/feast-dev/feast-fraud-tutorial/blob/main/notebooks/Fraud_Detection_Tutorial.ipynb) |
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