Overview
This Guidance shows how to build a serverless workflow to identify patterns of fraudulent activity within streaming data through both micro- and macroanalysis. Amazon Timestream helps analyze microlevel indicators, such as activities occurring within a short timeframe. Amazon Neptune then uses that data to flag potentially fraudulent activity within a macrolevel fraud graph, and performs in-depth, context-specific analysis on that flagged data. By using these services in tandem, you can improve detection capabilities and enrich the analysis of fraud impact. This Guidance can also apply to other uses requiring both micro- and macrolevel analysis, such as customer data platforms and trading risk platforms.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
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.
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.
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