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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.
You will need an AWS account to use this solution. Sign up for an account here.
To run this JumpStart 1P Solution and have the infrastructure deploy to your AWS account you will need to create an active SageMaker Studio instance (see Onboard to Amazon SageMaker Studio). When your Studio instance is Ready, use the instructions in SageMaker JumpStart to 1-Click Launch the solution.
The solution artifacts are included in this GitHub repository for reference.
Note: Solutions are available in most regions including us-west-2, and us-east-1.
Caution: Cloning this GitHub repository and running the code manually could lead to unexpected issues! Use the AWS CloudFormation template. You'll get an Amazon SageMaker Notebook instance that's been correctly setup and configured to access the other resources in the solution.
The project architecture deployed by the cloud formation template is shown here.
The project is divided into two main modules.
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.
sagemaker-graph-fraud-detection.yaml: Creates AWS CloudFormation Stack for solution
index.py: Lambda function script for invoking SageMaker processing
index.py: Lambda function script for invoking SageMaker training
mlp-fraud-baseline.ipynb: Jupyter notebook for feature based MLP baseline method using SageMaker and MXNet
mlp_fraud_entry_point.py: python entry point used by the MLP baseline notebook for MXNet training/deployment
utils.py: utility functions for baseline notebooks
xgboost-fraud-entry-point.ipynb: Jupyter notebook for feature based XGBoost baseline method using SageMaker
Dockerfile: Describes custom Docker image hosted on Amazon ECR for SageMaker Processing
build_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
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 labels
estimator_fns.py: Contains functions for parsing input from SageMaker estimator objects
graph.py: Contains functions for constructing DGL Graphs with node features and edge lists
requirements.txt: Describes Python package requirements of the Amazon SageMaker training instance
sampler.py: Contains functions for graph sampling for mini-batch training
train_dgl_mxnet_entry_point.py: python entry point used by the notebook for GNN training with DGL mxnet backend
utils.py: python script with utility functions for computing metrics and plots
config.py: python file to load stack configurations and pass to sagemaker notebook
requirements.txt: Describes Python package requirements of the SageMaker notebook instance
setup.py: setup sagemaker-graph-fraud-detection as a python package
dgl-fraud-detection.ipynb: Orchestrates the solution. Triggers preprocessing and model training
setup.sh: prepare notebook environment with necessary pre-reqs
This project is licensed under the Apache-2.0 License.