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Detecting Financial Fraud and Money Laundering with Deep Learning and GANs

Financial fraud and money laundering detection is a complex task that requires adapting techniques to identify new patterns. In this article, we will explore how deep learning (DL) and generative adversarial networks (GANs) can be applied to detect financial fraud and money laundering.

Challenges in Modeling Fraud as a Binary Classification Problem

Modeling fraud as a binary classification problem is not straightforward due to several challenges:

• Massive class imbalance: The number of legitimate transactions far exceeds the number of fraudulent ones, making it difficult for traditional machine learning algorithms to learn accurate models.
• Non-stationarity: The patterns of financial transactions are constantly changing, requiring techniques that can adapt quickly to new patterns.

Introducing Hopsworks: An Open-Source Platform for Detecting Fraud using GANs

Hopsworks is an open-source platform that provides a full end-to-end example for detecting fraud using GANs. The example includes:

Sample Raw Dataset of Financial Transactions

A sample raw dataset of financial transactions is provided, which can be used as input to the GAN.

Feature Engineering Programs

Feature engineering programs are included to compute complex features such as graph embedding and store them in a feature store. This allows for efficient storage and retrieval of features.

Notebooks for Hyperparameter Tuning

Notebooks are provided to find good hyperparameters for the GANs, enabling users to fine-tune the model for optimal performance.

Distributed Training of a GAN using Many GPUs

The platform supports distributed training of a GAN using many GPUs, which can accelerate neural network training and provide almost linear scaling when applying multiple GPUs to the problem.

Key Takeaways

• Financial fraud and money laundering detection is a complex task that requires adapting techniques to identify new patterns.
• Hopsworks provides a full end-to-end example for detecting fraud using GANs.
• The example includes feature engineering, hyperparameter tuning, and distributed training of a GAN.
• NVIDIA GPUs can accelerate neural network training and provide almost linear scaling when applying multiple GPUs to the problem.

Conclusion

We hope this article has provided valuable insights into the application of deep learning and GANs for detecting financial fraud and money laundering. We encourage readers to share their experiences with this important use case and state-of-the-art approach.