Many online businesses lose billions annually to fraud, but machine learning based fraud detection models can help businesses predict what interactions or users are likely fraudulent and save them from incurring those costs.
In this project, we formulate the problem of fraud detection as a classification task on a heterogeneous interaction network. The machine learning model is a Graph Neural Network (GNN) that learns latent representations of users or transactions which can then be easily separated into fraud or legitimate.
This project shows how to use Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a GNN model to detect fraudulent transactions in the IEEE-CIS dataset.
See the details page to learn more about the techniques used, and the online webinar or tutorial blog post to see step by step explanations and instructions on how to use this solution.
To get started quickly, use the following quick-launch link to create a CloudFormation stack and deploy the resources in this project.
Region | Stack |
---|---|
US East (N. Virginia) | |
US East (Ohio) | |
US West (Oregon) |
On the stack creation page, verify that the Launch Classic SageMaker Notebook Instance field under SageMaker configurations is set to 'true', check the boxes to acknowledge creation of IAM resources, and click Create Stack.
Once the stack is created, go to the Outputs tab and click on the SageMakerNotebook link. This will open up the jupyter notebook in a SageMaker Notebook instance where you can run the code in the notebook.
The project architecture deployed by the cloud formation template is shown here.
The project is divided into two main modules.
The first module uses Amazon SageMaker Processing to do feature engineering and extract edgelists from a table of transactions or interactions.
The second module shows how to use DGL to define a GNN model and train the model using Amazon SageMaker training infrastructure.
The jupyter notebook shows how to run the full project on an example dataset.
The project also contains a cloud formation template that deploys the code in this repo and all AWS resources needed to run the project in an end-to-end manner in the AWS account it's launched in.
deployment/
sagemaker-graph-fraud-detection.yaml
: Creates AWS CloudFormation Stack for solution
source/
lambda/
data-preprocessing/
index.py
: Lambda function script for invoking SageMaker processing
graph-modelling/
index.py
: Lambda function script for invoking SageMaker training
sagemaker/
baselines/
mlp-fraud-baseline.ipynb
: Jupyter notebook for feature based MLP baseline method using SageMaker and MXNetmlp_fraud_entry_point.py
: python entry point used by the MLP baseline notebook for MXNet training/deploymentutils.py
: utility functions for baseline notebooksxgboost-fraud-entry-point.ipynb
: Jupyter notebook for feature based XGBoost baseline method using SageMaker
data-preprocessing/
container/
Dockerfile
: Describes custom Docker image hosted on Amazon ECR for SageMaker Processingbuild_and_push.sh
: Script to build Docker image and push to Amazon ECR
graph_data_preprocessor.py
: Custom script used by SageMaker Processing for data processing/feature engineering
sagemaker_graph_fraud_detection/
dgl_fraud_detection/
model
mxnet.py
: Implements the various graph neural network models used in the project with the mxnet backend
data.py
: Contains functions for reading node features and labelsestimator_fns.py
: Contains functions for parsing input from SageMaker estimator objectsgraph.py
: Contains functions for constructing DGL Graphs with node features and edge listsrequirements.txt
: Describes Python package requirements of the Amazon SageMaker training instancesampler.py
: Contains functions for graph sampling for mini-batch trainingtrain_dgl_mxnet_entry_point.py
: python entry point used by the notebook for GNN training with DGL mxnet backendutils.py
: python script with utility functions for computing metrics and plots
config.py
: python file to load stack configurations and pass to sagemaker notebookrequirements.txt
: Describes Python package requirements of the SageMaker notebook instancesetup.py
: setup sagemaker-graph-fraud-detection as a python package
dgl-fraud-detection.ipynb
: Orchestrates the solution. Triggers preprocessing and model trainingsetup.sh
: prepare notebook environment with necessary pre-reqs
This project is licensed under the Apache-2.0 License.