Compliance Without Shackles: Why Finance Is Betting Big on Synthetic Data Description

A New Kind of Raw Material

Picture this: you’re testing a bank’s fraud detection model. Normally, you’d need millions of sensitive transactions. With synthetic data, you generate them instantly—every pattern intact, every anomaly preserved, but no real person exposed. That simple swap transforms the equation. Suddenly, compliance isn’t a barrier. It’s the starting point for faster development. What began as a trick for data scientists who wanted to protect privacy has matured into something much larger. Synthetic data is no longer a workaround. It’s infrastructure. And it’s growing fast—the market is expanding at 35–40% annually, projected to reach as much as $6.6 billion in the next decade. Those aren’t hype-driven numbers; they reflect live deployments solving billion-dollar problems.

Why Now? The Pressure Cooker of Regulation

The timing is no accident. Regulations have hardened. The EU AI Act, effective August 2024, classifies credit scoring, fraud detection, and anti-money laundering models as “high-risk.” That means stringent testing, meticulous documentation, and the looming threat of eye-watering fines—up to €35 million or 7% of global turnover. For a global bank, that’s a half-billion-dollar risk in a single audit.

And Europe isn’t alone. U.S. regulators have long demanded rigorous model validation under SR 11-7. The UK’s Financial Conduct Authority is pushing in the same direction. Privacy laws from GDPR to local data rules only complicate the picture further. The result? A compliance environment where using real customer data for testing has become both risky and ruinously expensive. Add to this the hard economics of data breaches. In 2024, a single breach in financial services averaged $6.08 million in costs. Every copy of real data you move multiplies the chance of that happening. At this point, relying on anonymization feels like locking the front door but leaving the windows wide open. Synthetic data closes those windows.

More Than Just Playing Defense

But here’s the twist: compliance may be the spark, yet the real story is performance. Synthetic data often outperforms the real thing. Why? Because you can bend reality. You can generate edge cases that history hasn’t yet recorded, simulate climate shocks, or craft new fraud patterns that haven’t surfaced in the wild.

Companies are already proving the upside. Mastercard cut its testing exposure by 84% while keeping test quality intact. Paytient, a credit provider, achieved a 3.7x ROI thanks to fewer compliance reviews and faster developer cycles. McKinsey found banks using synthetic data shaved 65% off their AI development timelines. Those aren’t incremental gains. They’re structural shifts in how products get built. When regulators start providing synthetic data themselves, you know the tide has turned. In the UK, the FCA’s Digital Sandbox pilot revealed something striking: 92% of participants rated synthetic data as the most valuable feature of the program—even though the datasets were only of “minimum standard” quality. The Bank for International Settlements went further with Project Aurora. Using synthetic data, they tested modern anti-money laundering models and found they could detect up to three times more illicit activity while cutting false positives by 80%. For compliance teams drowning in useless alerts, that’s nothing short of revolutionary.

The Market Votes With Its Wallet

If regulators are the stick, vendors are the carrot. In late 2024, analytics giant SAS acquired synthetic data startup Hazy. That’s not a hobby acquisition—it’s a signal that enterprise-grade synthetic data has become must-have infrastructure. Leaders like MOSTLY AI and Tonic.ai are also shaping the field. MOSTLY AI’s models preserve sequential patterns in transaction data better than competitors, a critical feature when testing financial systems that unfold over time. Tonic.ai focuses on seamless integration with legacy databases and modern cloud platforms alike, meeting banks where they actually operate. The line is clear: the winners won’t just sell generators. They’ll sell proof—datasets with audit-ready documentation, statistical fidelity, and privacy guarantees that can stand up to examiners’ checklists.

For banks just starting out, the roadmap is straightforward. Phase one: run pilots in safe areas—model development, stress testing, internal collaboration. Phase two: bring synthetic data into production workflows—validation pipelines, regulatory reporting, vendor partnerships. Phase three: flip the paradigm. Start with synthetic data as the default, bring in real data only for final confirmation. That last step is profound. It means compliance isn’t an afterthought—it’s baked into the development process. Institutions that reach this stage won’t just keep regulators satisfied. They’ll outpace competitors by moving faster, testing more, and failing safer.

Looking Forward

The trajectory is obvious. The EU AI Act’s strictest requirements kick in by 2027. The U.S. is moving toward its own AI regulations. Asia-Pacific regulators are building frameworks of their own. The message is consistent: testing must be rigorous, and privacy must be absolute. At the same time, technology keeps advancing. Generative AI is making synthetic data more realistic. Privacy-preserving techniques like differential privacy and federated learning are strengthening guarantees. The cost curve is dropping. Soon, synthetic-first development won’t just be possible—it’ll be expected. So the real question for financial institutions isn’t whether to adopt synthetic data. It’s how fast. Because in a world where compliance equals survival, those who embrace synthetic-first development won’t just dodge penalties. They’ll set the standards everyone else is forced to follow.

The revolution is already underway.

Sources for images: AI-synthetic-data-graphs.docx
Sources for research: FCA report on synthetic data, SAS blogs, MOSTLY AI, NextMSC, McKinsey, BIS Project Aurora, Deloitte, JPMorgan blog
Additional references: MarketsandMarkets, Grand View Research, Mordor Intelligence, Business Research Insights, BDO, Forvis Mazars