Architecture Overview - Fraud Detection Using Machine Learning

Architecture Overview

Deploying this solution and running the notebook builds the following environment in the AWS Cloud.


        Fraud Detection Using Machine Learning solution - architectural overview

Figure 1: Fraud Detection Using Machine Learning architecture on AWS

The AWS CloudFormation template deploys an example dataset of credit card transactions contained in an Amazon Simple Storage Service (Amazon S3) bucket and an Amazon SageMaker notebook instance with different ML models that will be trained on the dataset.

The solution also deploys an AWS Lambda function that processes transactions from the example dataset and invokes the two SageMaker endpoints that assign anomaly scores and classification scores to incoming data points. An Amazon API Gateway REST API triggers predictions using signed HTTP requests, and an Amazon Kinesis Data Firehose delivery stream loads the processed transactions into another Amazon S3 bucket for storage.

The solution also provides an example of how to invoke the prediction REST API as part of the Amazon SageMaker notebook.

Once the transactions have been loaded into Amazon S3, you can use analytics tools and services, including Amazon QuickSight, for visualization, reporting, ad-hoc queries, and more detailed analysis. For customers who want to use Amazon QuickSight to visualize the processed transactions, see Appendix A.

By default, the solution is configured to process transactions from the example dataset. To use your own dataset, you must modify the solution. For more information, see Customization.