This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
🚨 Boosting ML Model Robustness Against Money Laundering: The Power of Adversarial Training
Money laundering detection is a critical task in the fight against financial crimes. However, machine learning (ML) models can be vulnerable to attacks that compromise their accuracy and effectiveness. To enhance ML model robustness against money laundering detection, we can leverage the power of adversarial training with data augmentations.
The Adversarial Training Approach
Adversarial training involves exposing ML models to adversarial examples that mimic real-world attacks. By training the model on these examples, it learns to recognize and resist the attacks, thereby improving its robustness. In the context of money laundering detection, we can create adversarial examples that mimic common money laundering tactics, such as:
- Structured Transactions: Creating batches of transactions that follow a structured pattern, making it difficult for the model to detect suspicious activi...
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This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez | Sciencx (2025-10-30T16:03:31+00:00) 🚨 Boosting ML Model Robustness Against Money Laundering: The. Retrieved from https://www.scien.cx/2025/10/30/%f0%9f%9a%a8-boosting-ml-model-robustness-against-money-laundering-the/
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