Banks don't have an AI problem — they have an approval problem.
In many cases, AI models already outperform existing systems, whether in fraud detection, customer service or internal decision support. Still, a substantial number never reach production. Progress tends to slow once model validation or compliance review begins, and often it doesn't recover.
We see this play out repeatedly. Organizations still lack a clear way to move AI projects into production.
Most governance structures in banking were designed for predictable systems. Traditional models are relatively straightforward: Their logic can be traced step by step, and their behavior is easier to explain and document.
AI systems simply behave differently. They rely on massive data sets, evolve over time, and don't always produce results that can be explained in simple terms. That creates friction as soon as they enter the internal review stage.
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It is further complicated by regulatory expectations. Banks operate under strict supervisory frameworks set by financial authorities, yet guidance on AI is still evolving and often inconsistent across markets. That lack of clarity makes internal approval even harder.
The review process itself hasn't changed, but AI makes it harder to apply. Questions start to pile up:
How does the system behave over time?
Can decisions be reproduced?
Who is responsible for the outcome?
If these points aren't clear, decisions tend to stall. It's not because the model is rejected; it's because no one is comfortable approving it. Such hesitation is particularly pronounced in a highly regulated industry such as banking. Here, organizations are structurally risk-averse, and accountability sits high.
Explainability is frequently framed as technical capacity. In practice, it determines whether a system can go live.
For an AI model to pass validation, reviewers need to understand how it arrives at a decision and whether that decision would stand up to scrutiny. That includes tracing outputs back to input data, understanding how edge cases are handled, and ensuring that results remain consistent over time.
This is where many otherwise strong initiatives break down.
In many banks, fraud detection models perform well in testing but do not perform well in production. The issue isn't performance; it's the difficulty of explaining individual decisions in a way that meets audit requirements.
The limitation isn't accuracy; it's auditability. In some cases, this gap delays deployment for months, even when the model is already exceeding existing systems.
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This becomes even more complex in customer-facing use cases. With conversational AI, generating correct responses is only part of the challenge. Systems also need to operate within strict security and compliance boundaries, while ensuring that every interaction remains traceable, especially when actions such as payments or account changes are involved.
One pattern shows up frequently: Governance is treated as something to address only at the very end.
Teams build a model, prove that it works and only then try to align it with internal requirements. That approach creates problems later, particularly when sensitive data or customer-facing use cases are involved.
In one recent engagement, an organization explored using large language models to analyze internal financial documents and support research workflows. The initial results looked promising. Once questions arose around data access, auditability and control, however, progress slowed and eventually stopped. Those issues hadn't been addressed up front, and resolving them took longer than building the model itself. That's not unusual; governance questions often surface only after the technical work is done.
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Banks that move AI into production take a different approach. Governance isn't treated as a final hurdle; it shapes how systems are built from the start.
Three practices tend to make a noticeable difference:
Start with use cases that are easy to validate, control and measure: Focus on high-volume, low-risk interactions such as balance inquiries or transaction status checks. These are predictable and easier to review, which makes them a practical way to test both the model and the approval process.
Define how decisions will be documented and reviewed from the start: Before building the model, clarify how outcomes will be traced back to data, how decisions can be explained and how the system will be monitored over time.
Set clear boundaries for human involvement: Routine tasks can be automated, but more sensitive actions should be escalated to human review. Clear boundaries make accountability easier and simplify internal approval.
It's easy to frame this as a lack of trust in AI. In reality, it's more concrete than that. Most banks already know how to build effective AI systems. What's missing is a reliable way to evaluate and approve them.
Until that changes, models will continue to perform well inside controlled environments but fail to reach production. It's not because they don't work, it's because the organization can't sign off on them.
As long as approval remains unpredictable, banks will keep investing in AI that never generates real returns. The banks that solve this won't just deploy AI more safely. They'll deploy it faster and at scale.
Avenga
Mike Crosby is director of business development for banking and financial services at Avenga. He works with financial institutions to deploy AI and advanced analytics in these highly regulated environments, focusing on governance, explainability and production readiness.
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