Without guardrails, AI in finance is just expensive guesswork
As finance races to adopt AI, most conversations focus on speed, scale and ROI. But beneath the automation hype lies a quiet, more urgent challenge: governance. Without it, AI in finance risks becoming a black box of unexplainable decisions, compliance gaps and unchecked bias, all in the name of efficiency.
The next generation of finance leaders won’t just deploy AI tools; they’ll design the oversight systems that make those tools trustworthy, transparent and auditable. From establishing model validation protocols to defining human-in-the-loop decision rights, governance is the infrastructure CFOs need to scale AI safely and credibly.
Payhawk firmly believes that the EU’s new GPAI regime has created predictable guardrails for serious builders that ‘right-size’ their AI models and invested early in purpose-built AI.
With the Act turning must have features like explainable, auditable and privacy-proof features into market standards, article will look at why “governance-first AI” is the only viable path in regulated environments, practical frameworks for building AI guardrails in finance and why early investments in governance drive long-term speed, not friction.
Most CFOs today sit at a crossroads. On one hand, 92% of organisations report positive ROI from AI pilots. On the other, only 4% are actually scaling those pilots across the enterprise. The gap is not about technology. It’s about trust. AI can only become core to finance if it is explainable, auditable, and aligned with compliance standards. Without these guardrails, every automated forecast, anomaly alert, or spend classification risks becoming a black box. With them, AI becomes a controllable, strategic asset.
This is why the role of the CFO is changing. Traditionally the steward of capital, the CFO must now become the steward of algorithms. That doesn’t mean writing code, but it does mean owning the accountability model for how AI is deployed inside finance. It means asking what data is training models, what assumptions are embedded in forecasts, who signs off when machines make decisions, and whether outputs can be explained to a regulator or an auditor. In the age of AI, the CFO is no longer just the financial gatekeeper, they are the system architect of trust.
Regulation is often viewed as a drag on innovation, but the opposite is true. The EU AI Act and the new GPAI regime are not roadblocks; they are blueprints. They create clarity and predictability in a space that until now has thrived on hype and opacity. With governance standards becoming law, CFOs have both the obligation and the opportunity to get ahead. Those who design explainability, auditability, and fairness into their AI systems from day one will not just stay compliant, they will move faster, scale more confidently, and win the trust of boards, regulators, and markets.
The concept of “right-sized AI” captures this shift well: purposeful design, human-centric architecture, and embedded governance. Purposeful design means wrapping large models in narrowly scoped finance agents with least-privilege access. Human-centric architecture means CFOs remain firmly in control, with AI surfacing insights and humans approving commitments. Embedded governance means mapping every input, decision path and outcome to audit requirements, ready for regulator or client review from day one. Far from being a constraint, the GPAI rulebook is accelerating those who invested early in purpose-built, governed AI.
The cost of ignoring governance is already visible. A misclassified expense that distorts quarterly reporting, a risk model that bakes in hidden bias, an autonomous payment approved outside policy, these are not glitches. They are business failures. Moreover, when they happen under AI, they are harder to detect, harder to explain and harder to fix. Governance is the difference between AI as an accelerant and AI as a liability.
The competitive advantage belongs to those who are governable. Only a handful of finance teams are truly scaling AI today, even though the vast majority see positive pilot results. The bottleneck is not capability but trust. Governance is the lever that unlocks scale. Teams that invest early in explainability and oversight are the ones accelerating forecasting cycles, reducing leakage, catching fraud faster, and doing it all in ways that stand up to scrutiny.
Europe shows how this plays out at scale. The GPAI rules turn trust into a design specification. Compliance becomes a feature customers can rely on, not a tax. A single EU conformity assessment now unlocks 27 markets, replacing the patchwork of national regimes. And investors, allergic to uncertainty, reward the clarity. Far from handicapping innovation, Europe’s model turns reliability into its edge, proving that capital follows certainty, not laxity.
CFOs don’t need to wait for perfect tools to get started, but they do need to demand governable ones. In a world of opaque algorithms and rising regulatory scrutiny, the most powerful claim a finance leader can make is not that their AI works. It’s that their AI can be trusted. The winners in this next phase of finance will not be those who move fastest, but those who move with confidence, building AI that is explainable, auditable, and aligned with both the letter and the spirit of regulation. Governance is not the brake on AI adoption. It is the steering wheel.