AI governance fails without clean data. Learn why financial services need reliable, unified data infrastructure—and how Finray enables accurate, stable AI.
A recent Wall Street Journal opinion piece argues that as AI systems grow more complex, demanding perfect interpretability is unrealistic. Instead, regulators and practitioners should focus on controllability, setting boundaries, monitoring behavior, and intervening when needed.
They are right about the limits of interpretability. But a deeper truth remains: Neither interpretability nor controllability works if the underlying data is wrong.
In financial services, AI is only as governable and reliable as the data it consumes. This makes modern data infrastructure for financial services essential.
As the WSJ article notes, the release of DeepSeek's model—born out of a quantitative trading firm—highlights that finance is already one of the most advanced proving grounds for AI. The techniques that powered DeepSeek were shaped inside markets where models must learn, adapt, and compete under real pressure.
As such, financial markets offer an unusually demanding environment defined by:
Every regulatory shift, political event, or macro change resets the landscape. If AI can adapt here, it can adapt anywhere. So finance is not only being transformed by AI—it's where the next generation of AI is being shaped.
Yet, the interpretability-versus-controllability debate misses the bigger issue.
Before asking whether we can interpret or control a model, we should ask: What data is going into the model in the first place?
Even the most advanced systems follow the simplest rule: Garbage in, garbage out. In financial services, where AI influences pricing, liquidity, and risk, bad data is not a nuisance; it's a threat.
In fact, according to a report from The Institute of International Finance published in January 2025, data quality and availability gaps remain the biggest challenges for financial institutions seeking to adopt AI in production.
An opaque model based on inconsistent, fragmented, or unreconciled inputs is dangerous, even with controls in place. Instead of preventing chaos, you're managing chaos more efficiently.
The debate around AI governance often treats interpretability and controllability as opposing philosophies, but both depend on something far more basic: a data foundation that can support them.
This creates consistency across systems, preserves lineage, reduces ambiguity, and provides teams with a stable reference point for understanding and overseeing model behavior. At Finray, our Common Data Model (CDM) is designed to ensure financial data is:
We're not adding AI to messy pipelines; we're rebuilding the underlying structure, which strengthens both sides of the AI governance discussion.
Interpretability focuses on understanding why a model produced a particular output. In financial services, that effort becomes almost impossible when the inputs themselves are noisy, inconsistent, or incomplete. The most effective way to improve interpretability is not to peer deeper into the model, but to remove the ambiguity created by bad data before the model ever runs.
Take bond pricing, for example. You may not be able to explain why a model selected a specific spread, but if the trades, positions, and market feeds it relies on are already reconciled and trustworthy, you know the model is responding to real market signals rather than stale prices, duplicated trades, or mismatched quantities. Clean inputs don't make the model transparent, but they make its behavior far easier to evaluate and challenge.
With reliable data as the starting point, interpretability becomes clearer and more meaningful.
Controls only work when the data entering the system is trustworthy. High-integrity data gives controls something solid to act on. When the underlying information is accurate and consistent, alerts trigger for the right reasons, thresholds behave predictably, and intervention mechanisms actually help rather than create noise or confusion.
Transparent, well-structured data also makes opaque models safer to operate. Even if you cannot fully explain how a model arrived at a specific prediction or recommendation, you can still trust that its output is based on accurate inputs rather than hidden data errors.
This is the overlooked irony of AI governance in finance: people focus heavily on guardrails, monitors, and intervention mechanisms, and these controls are essential. But they only work when the data feeding the model is clean, consistent, and auditable.
The foundation of safe AI is not the guardrails alone, but the combination of strong oversight and high-integrity data. Without trustworthy inputs, even the best controls cannot keep a model on course.
The article authors are right that perfect interpretability is unrealistic as models become more complex. But accepting black-box AI does not mean accepting black-box data. Even if the inner workings of a model are harder to unpack, the inputs that feed it should remain clear and trustworthy. The less interpretable AI becomes, the more important transparent and reliable data infrastructure becomes, because it provides stability and clarity even when model behavior is harder to dissect.
With a unified, trustworthy foundation, firms can:
Better data is not a compliance expense; it's an innovation multiplier.
Finance will continue to challenge AI systems in nonstationary environments, adversarial dynamics, and constant change. Innovation and governance are not opposites. They depend on the same foundation: data you can trust.
This is the problem Finray solves.
We enable financial platforms to:
In the age of AI, your models are only as good as the infrastructure beneath them.
If your AI strategy is limited by fragmented or unreliable data, Finray can help. We turn disconnected systems into clean, unified, AI-ready financial data so you can innovate with confidence.
Contact Finray for a demo today.
Why isn't interpretability enough for AI in financial services?
Because unclear or inconsistent inputs make explanations meaningless. Clean data is what makes model behavior evaluable.
Why are financial markets a proving ground for AI?
They combine constant data, immediate consequences, shifting conditions, and competitive pressure—forcing models to adapt in real time.
How does bad data weaken AI controllability?
Controls can't function if inputs are wrong. Reliable data makes alerts, thresholds, and interventions work as intended.
Why is strong data infrastructure an "innovation multiplier"?
It enables better models, fewer silent errors, stronger governance, and safer experimentation.
How does Finray help?
Finray transforms AI performance by providing the clean, auditable, GL-ready data models depend on to function accurately, stably, and accelerate real intelligence.