Guidance for Near Real-Time Fraud Detection Using Amazon Redshift Streaming Ingestion
Overview
This Guidance demonstrates how to use machine learning (ML) to combat fraudulent financial transactions. With AWS services, financial institutions can create an application that simulates credit card transactions. This application enables financial institutions to train and develop an ML model capable of generating near-real-time inferences. Financial institutions can then identify fraudulent transactions and use ML to predict fraud before it strikes.
How it works
Use machine learning models trained on historical data to combat financial fraud in near real-time.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
Related Content
Near-real-time fraud detection using Amazon Redshift Streaming Ingestion with Amazon Kinesis Data Streams and Amazon Redshift ML
This post demonstrates how Amazon Redshift can deliver streaming ingestion and machine learning (ML) predictions all in one platform.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
Did you find what you were looking for today?
Let us know so we can improve the quality of the content on our pages