What does this AWS Solution Implementation do?
Fraud Detection Using Machine Learning deploys a machine learning (ML) model and an example dataset of credit card transactions to train the model to recognize fraud patterns. The model is self-learning which enables it to adapt to new, unknown fraud patterns.
You can use this solution implementation to automate the detection of potentially fraudulent activity, and the flagging of that activity for review. The solution implementation is easy to deploy and includes an example dataset but you can modify the solution implmentation to work with any dataset.
AWS Solution Implementation overview
Fraud Detection Using Machine Learning enables you to execute automated transaction processing on an example dataset or your own dataset. The included ML model detects potentially fraudulent activity and flags that activity for review. The diagram below presents the architecture you can automatically deploy using the solution’s implementation guide and accompanying AWS CloudFormation template.
Fraud Detection Using Machine Learning architecture
This solution includes an AWS CloudFormation template that deploys an example dataset of credit card transactions contained in an Amazon Simple Storage Service (Amazon S3) bucket and an Amazon SageMaker endpoint with an ML model that will be trained on the dataset.
The solution also deploys an Amazon CloudWatch Events rule that is configured to run every minute. The rule is configured to trigger an AWS Lambda function that processes transactions from the example dataset and invoke the Amazon SageMaker endpoint which predicts whether those transactions are fraudulent based on the trained ML model. An Amazon Kinesis Data Firehose delivery stream loads the processed transactions into another Amazon S3 bucket for storage.
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.
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 the deployment guide.
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