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.
Use this Guidance to automate the detection of potentially fraudulent activity, and the flagging of that activity for review. Fraud Detection Using Machine Learning is easy to deploy and includes an example dataset but you can modify the code to work with any dataset.
Fraud Detection Using Machine Learning allows you to run 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 build using the example code on GitHub.
Fraud Detection Using Machine Learning architecture
The code deploys the following infrastructure:
- An Amazon Simple Storage Service (Amazon S3) bucket containing an example dataset of credit card transactions.
- An Amazon SageMaker notebook instance with different ML models that will be trained on the dataset.
- An AWS Lambda function that processes transactions from the example dataset and invokes the two Amazon SageMaker endpoints that assign anomaly scores and classification scores to incoming data points.
- An Amazon API Gateway REST API invokes predictions using signed HTTP requests.
- An Amazon Kinesis Data Firehose delivery stream loads the processed transactions into another Amazon S3 bucket for storage.
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