Fraud Detection with Red Hat OpenShift AI

Introduction

Welcome!

In this lab, you will learn how to incorporate data science and artificial intelligence and machine learning (AI/ML) into an OpenShift development workflow.

You will use an example fraud detection model to complete the following tasks:

  • Train a fraud detection model by using Jupyter Notebooks.

  • Deploy the model by using OpenShift AI single-model serving. (new feature)

  • Refine and train the model by using automated pipelines. (recently added feature)

And you will do all of this without having to install anything on your own computer, thanks to Red Hat OpenShift AI.

About the example fraud detection model

The example fraud detection model monitors credit card transactions for potential fraudulent activity. It analyzes the following credit card transaction details:

  • The geographical distance from the previous credit card transaction.

  • The price of the current transaction, compared to the median price of all the user’s transactions.

  • Whether the user completed the transaction by using the hardware chip in the credit card, entered a PIN number, or for an online purchase.

Based on this data, the model outputs the likelihood of the transaction being fraudulent.