Why Most Banks Get AI Governance Wrong - And How to Fix It
In a recent article, I wrote about how the biggest challenges in credit risk modelling rarely sit in the algorithms themselves, but instead arise from the way models are developed, documented and understood within organisations.
What becomes clear when you follow that line of thinking a step further is that many of the governance challenges banks face today are not separate issues at all - they are a direct consequence of that same underlying friction.
Over the past few years, my conversations with banks about AI and modelling have tended to follow a very familiar pattern, where there is clear enthusiasm about doing more with advanced analytics and machine learning, but that enthusiasm is almost always accompanied by a concern that governance is somehow getting in the way of progress.
It is easy to understand where that perception comes from, given that governance is so often associated with additional process, heavier documentation requirements and increased levels of oversight, all of which can feel like unnecessary friction in what is intended to be a fast-moving and innovative space.
However, that framing does not quite capture the reality of what is actually happening in most organisations. In practice, governance itself is rarely the thing that slows banks down; rather, it is the absence of the right kind of governance, applied in the right way, that creates the issues.
Trust, Not Technology, Is the Constraint
Across a wide range of institutions, I see a remarkably consistent story play out, where models are often developed relatively quickly and, in many cases, to a high technical standard, but then begin to lose momentum once they move beyond the development phase and into validation, review and approval.
At that point, timelines begin to stretch, documentation is revisited multiple times, and there is often a prolonged back-and-forth between modelling teams, risk functions and audit, which can significantly slow progress and create frustration on all sides.
What is notable is that this rarely happens because the model itself is fundamentally flawed or unfit for purpose; instead, it tends to occur because the model is difficult to explain, inconsistently documented or lacks the level of supporting evidence required to give stakeholders sufficient confidence in its use.
When viewed through that lens the issue becomes much clearer, because at its core this is not a problem of technology or modelling capability, but rather one of trust.
For AI and advanced analytics to deliver meaningful value within a banking environment they must be trusted not only by the teams building the models, but also by those responsible for overseeing and challenging them, including risk, audit, senior management and ultimately regulators, without which even the most sophisticated models will struggle to move forward.
Regulatory Expectations Are Nothing New
What makes this challenge particularly interesting is that regulatory expectations in this area have been well established for some time, and should not come as a surprise to most institutions operating in this space.
Supervisory frameworks have consistently emphasised the importance of strong lifecycle governance, independent validation and clear accountability, while regulators continue to push for greater transparency, consistency and full auditability across models and the processes that support them.
If you strip those expectations back to their essence the underlying message is relatively simple, which is that if a model cannot be clearly explained, properly evidenced and effectively governed then it will be extremely difficult to scale in a controlled and sustainable way.
The Misconception About Governance
One of the most persistent misconceptions I encounter is the idea that governance and innovation are somehow in tension with one another, and that improving one inevitably comes at the expense of the other.
In reality, my experience has consistently shown the opposite to be true, particularly in environments where governance is either weak, inconsistent or applied too late in the process.
In those situations, each model effectively becomes a bespoke exercise, with different teams following different approaches, documentation varying in both structure and quality, and validation becoming inherently unpredictable because there is no consistent baseline against which models can be assessed.
As a result, questions tend to emerge late in the process, approvals take longer than expected and timelines extend in ways that are difficult to control.
By contrast, when governance is properly embedded and applied consistently, the entire process becomes more structured and predictable, with clearer expectations from the outset, evidence being generated as part of development rather than retrospectively and validation becoming more efficient because much of the required groundwork has already been done.
Seen in this way, good governance does not introduce friction into the process, but instead removes a significant amount of it.
What Leading Banks Are Doing Differently
The banks that are making the most progress in this area have typically made a subtle but important shift in how they think about governance, moving away from treating it as something that happens after a model has been built and towards embedding it directly within the modelling process itself.
This shift has a meaningful impact on how models are developed and progressed through the organisation, because it ensures that explainability is considered from the outset, that development follows a consistent and repeatable framework rather than individual preference, and that documentation evolves alongside the model rather than being left until the final stages.
It also allows important considerations such as fairness, transparency and robustness to be addressed early in the lifecycle, rather than being retrofitted later under time pressure.
While this may not appear to be a dramatic change in theory, in practice it significantly improves the speed, consistency and confidence with which models move through validation and into production.
Embedding Governance Into the Process
This way of thinking is increasingly reflected in how modern modelling platforms are designed, with a growing emphasis on making transparency, explainability and governance integral to the modelling workflow rather than treating them as separate layers that sit on top.
When governance is embedded directly into how models are developed, the benefits become immediately apparent, as models are not only more robust from a technical perspective, but are also far easier to validate, explain and deploy within a controlled environment.
In the context of banking where scrutiny and accountability are critical, these qualities are often just as important as the underlying predictive performance of the model itself.
From Bottleneck to Enabler
When governance is approached in this way its impact is both tangible and measurable, with validation cycles becoming shorter, audit challenges becoming less frequent and confidence across stakeholders improving in a way that allows decisions to be made more quickly and with greater certainty.
Perhaps most importantly models are far more likely to make it into production, which is ultimately where their value is realised.
As a result, governance begins to be seen not as a bottleneck that restricts progress but as a foundational capability that enables organisations to scale their use of AI in a controlled and sustainable manner.
A Final Thought
AI will undoubtedly continue to play a transformative role in banking over the coming years, but success will not simply be defined by which organisations are able to build the most sophisticated models.
Instead it will be determined by those that can deploy those models with confidence, consistency and control, in a way that satisfies both internal stakeholders and external regulators. And ultimately, achieving that comes down to one thing: building trust through effective, well-embedded governance.
This is why the conversation about AI governance cannot be separated from the modelling environment itself. As discussed previously, the real challenges rarely sit in the algorithms - they sit in the processes and structures around them. Governance simply brings those challenges into sharper focus.
To see how Paragon’s Modeller supports modelling governance, visit www.credit-scoring.co.uk/modeller