What is the Financial Fraud Training Container?

The Financial Fraud Training container provides the capabilities to train Graph Neural Network (GNN) and XGBoost models to predict fraud scores of credit card transactions. Based on user provided training configuration, the container first builds a GNN model that produces embeddings for credit card transactions, and then the container uses the transaction embeddings to train an XGBoost model to predict fraud scores of the transactions. The container encapsulates the complexity of creating the graph in cuGraph. Once the graph is created, the GNN model is trained and used to produce the embeddings that are then feed to XGBoost.

Applications

Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade. Traditional fraud detection methods, which rely on rules-based systems, or statistical methods, are reactive and increasingly ineffective in identifying sophisticated fraudulent activities. As data volumes grow and fraud tactics evolve, financial institutions need more proactive, intelligent approaches to detect and prevent fraudulent transactions.

Getting started

Pull the container

docker login nvcr.io --username '$oauthtoken' --password $NGC_API_KEY

Run Locally

Run the following command to start the container. NOTE: Replace DATA_DIR and DIR_TO_SAVE_TRAINED_MODELS with actutal full paths.

docker run -it --rm --name=financial-fraud-training --gpus "device=0" -p 8000:8000 -e NIM_HTTP_API_PORT=8000 -p 50051:50051 -e NIM_GRPC_API_PORT=50051 -e NIM_DISABLE_MODEL_DOWNLOAD=True -v <DATA_DIR>:/data -v <DIR_TO_SAVE_TRAINED_MODELS>:/trained_models nvcr.io/nvidia/cugraph/financial-fraud-training:1.0.0 -e NGC_API_KEY=$NGC_API_KEY

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