Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are a class of machine learning models composed of two neural networks, the generator and the discriminator, that are trained simultaneously through a competitive process.
The generator creates synthetic data (e.g., images, text, or financial data) intended to resemble real data, while the discriminator evaluates whether the data it receives is real or generated. Over time, both networks improve: the generator becomes better at producing realistic outputs, and the discriminator becomes better at detecting fakes.
GANs are widely used in image generation, data augmentation, and anomaly detection. In FinTech, their potential lies in synthetic data generation, fraud detection, scenario simulation, and more.
Adversarial Learning Networks
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GANs
Examples
A global bank leverages GANs to generate synthetic transaction datasets for training fraud detection algorithms. Due to privacy regulations like GDPR, using real client data is heavily restricted. GANs enable the creation of anonymized yet realistic data that mimics customer behavior without exposing personal information. These datasets are used to train and test machine learning models, ensuring compliance and model performance without compromising security.
Another use case is in market simulation: GANs are trained on historical financial time series to generate plausible market conditions, enabling risk teams to stress-test portfolios under synthetic yet highly realistic market shocks.
FAQ
What are GANs used for in finance?
GANs are used to generate synthetic financial data, detect anomalies (like fraud or money laundering), and simulate various market scenarios for stress testing and risk modeling. They help overcome data scarcity and enhance privacy compliance.
How do GANs differ from other AI models in FinTech?
GANs are unique in that they create data rather than just analyse it. While traditional models predict or classify based on existing data, GANs can generate entirely new, realistic data, making them ideal for simulation, augmentation, and anonymisation tasks.
Are GANs safe to use in regulated industries like banking?
Yes - when properly implemented. GAN-generated data can help banks train models without using sensitive customer information, which is especially useful under data privacy laws. However, it's crucial to validate outputs and monitor for biases or inaccuracies.
