Banks are not short on data. They are short on data they can actually use for AI use cases.
That is one of the real constraints on AI in banking right now. Financial institutions have years of transaction history, customer behavior, fraud patterns, servicing activity, and risk data. The opportunity is obvious. The problem is that most banks still cannot move fast enough to use that data safely and practically once an AI use case moves beyond discussion and into delivery. The data is too sensitive, too fragmented, too locked down, or simply too slow to access. McKinsey estimates generative AI could unlock $200 billion to $340 billion annually in banking, but that value does not come from ambition alone.
This is why synthetic data is getting more attention. Not because it is flashy, but because it helps solve a practical problem. More banks are starting to treat it less like a privacy workaround and more like part of the delivery stack: something that reduces approval friction, improves test coverage, and makes experimentation possible when live data is too sensitive or too difficult to access.
In simple terms, synthetic data is newly generated data that preserves the patterns of real data without exposing actual customer records. That is what makes it useful for testing, model development, and controlled experimentation.
The clearest use cases are fraud and AML. In fraud detection, synthetic data helps teams simulate rare, fast-changing patterns that do not show up often enough in historical datasets. In AML, it gives banks a more realistic way to test typologies and monitoring logic when useful signals are spread across institutions and jurisdictions, but sensitive data cannot easily be shared.
Credit and stress testing are also relevant. Synthetic data can help banks build more representative training populations for thin-file or underrepresented borrowers, though it can also reproduce historical bias if the source data is weak. It also makes it easier to test beyond a narrow reading of the past when financial shocks do not arrive in familiar forms.
A year or two ago, synthetic data still felt like a sandbox topic. It does not anymore. The conversation is moving into real delivery discussions around fraud, AML, testing, risk, and model governance. The business case is also easier to understand than it used to be: faster proof-of-concept cycles, better rare-event coverage, and less waiting for access to live data.
None of this means leaders should get casual about it. Poorly generated or weakly validated synthetic data can create false confidence, embed bias, and produce results that look stronger in testing than they will in production.
My view is that some banks are still talking about AI scale as if the main constraint is model sophistication. More and more, that is not the real issue. The real issue is whether the institution has built a safe, usable, governed data layer that teams can actually build with. Synthetic data will not solve that on its own, but it is becoming an important part of the answer. The banks that get the most value from it will be the ones that start with a real use case, put proper governance around it from the beginning, and use it to solve an execution problem rather than chase a trend.
What We Are Doing Differently at Finaira
At Finaira, we see synthetic data as more than a privacy safeguard. In practice, it has become the primary accelerant for our delivery engine.
A lot of banking AI work slows down at the same point: waiting on internal InfoSec approvals before teams can get anywhere near sanitized production-level data. That caution makes sense, but it also creates drag early in the process. We use synthetic data to reduce some of that drag and get useful work moving sooner.
That is a big part of how we run our DOJO Impact Sprints. It gives our Forward Deployed Engineers a way to prototype, test, and show the ROI of complex Agentic AI workflows in a matter of days, often before real customer data is introduced. The point is not speed for its own sake. It is getting enough signal early to know what is worth taking further.
We also use synthetic generation to test edge cases and stress scenarios that may not appear often enough in historical datasets but still matter in production. It is part of making sure systems are not only innovative, but governed and safe to scale.
For us, synthetic data is not a side technique or a compliance workaround. It is one of the things that helps turn AI ambition into delivery.
References
- McKinsey research on generative AI value in banking
- FCA, Generating and Using Synthetic Data for Models in Financial Services: Governance Considerations