Why Synthetic Data Matters for Fintech

Banks sit on mountains of transaction data. They can’t share it — for good reason. Privacy regulations like GDPR and India’s DPDP Act make it illegal to hand over raw customer data, even for research.

But here’s the problem: you can’t build good ML models without good data.

The Gap

Startups building fraud detection, credit scoring, or risk assessment tools need realistic financial data to train their models. But they can’t get it. The data lives behind regulatory walls.

This creates a weird situation:

Banks have data but limited ML talent
Startups have ML talent but no data
Everyone loses

Enter Synthetic Data

Synthetic data generation creates new data that has the same statistical properties as real data — but doesn’t correspond to any actual person.

Think of it as learning the shape of the data without memorizing the individuals in it.

How It Works (Simplified)

Train a generative model (GAN or VAE) on real financial data
Apply differential privacy during training to ensure no individual record can be reverse-engineered
Generate new samples that look realistic but are entirely artificial
Validate that the synthetic data preserves the patterns that matter (transaction distributions, temporal correlations, fraud patterns)

Why This Is Hard

Financial data isn’t like images. You can’t just slap a GAN on it and call it done.

Tabular data has mixed types (categorical + continuous)
Temporal dependencies matter (transactions happen in sequences)
Rare events (fraud) are the most important — and the hardest to synthesize
Privacy guarantees need to be provable, not just “probably fine”

What I’m Building

My research focuses on building a pipeline that handles all of this:

GAN and VAE architectures adapted for tabular financial data
Differential privacy constraints baked into the training process
Evaluation metrics that go beyond “does it look right” to “does it actually work for downstream ML tasks”

More details coming soon as the project progresses.

If this interests you, I’d love to chat — reach out at ahana.bajpai@gmail.com.