Finance & Markets
representation, it also involves a series of modelling choices about what counts as ‘fair’ and how balance should be achieved. These decisions are not neutral. They can reshape patterns in the data in ways that are difficult to detect, especially once the data is used downstream in decision-making systems. For managers, this means synthetic data does not solve the problem of bias. It changes how bias enters the system. You should therefore test synthetic data just as rigorously as real data, paying close attention to how it has been generated, what assumptions have been made, and how those assumptions may affect outcomes, particularly when decisions impact customers or markets. These challenges matter for regulators as much as for firms. Most regulatory frameworks focus on model outputs and decision-making processes. But synthetic data sits earlier in the pipeline, at the point
where data is generated. This means important risks can be introduced before a model even starts operating. If regulation does not extend upstream, these risks remain invisible. Policymakers therefore need to rethink how they approach data in financial systems. Synthetic data should not be treated as a technical workaround. It is becoming part of the infrastructure of finance. Like credit ratings or benchmarks, it can shape behaviour across firms and create shared dependencies. If a small number of providers dominate this space, it raises concerns about market concentration and systemic vulnerability. The practical message for firms is straightforward. Treat synthetic data as a strategic asset. Ensure your organisation understands how it is produced and retains the ability to interrogate it. Do not focus only on whether your models perform well. Focus on where your data comes from,
how it is generated, and what assumptions it carries. If you cannot explain this clearly, you are taking on more risk than you realise. The broader shift is that AI in finance is no longer just about algorithms. It is about data: how it is constructed, shared, and governed. Synthetic data is a powerful tool, but it changes where risk is located rather than eliminating it. If you use that data without proper oversight, you risk building systems that are more opaque, more interconnected, and ultimately more fragile than before. As the saying goes: “Garbage in, garbage out.” Without careful governance, the same problem can easily emerge in modelling with synthetic data.
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