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.
