In the previous article, I explored confidence as the factor that determines whether models move smoothly through organisations or begin to slow. Confidence, however, doesn’t just help models progress. In my experience, its absence has very specific and predictable consequences. 

This article looks at what happens when that confidence is missing, not in theory, but in practice.  When models fail to progress inside organisations the failure is rarely dramatic. What I see more often is a gradual loss of momentum. Reviews take longer. Questions remain unresolved. Priorities shift. The model continues to exist, but progress quietly fades.  In effect, it stalls. 

Across the earlier articles in this series, I’ve been trying to unpack where modelling challenges are often misdiagnosed.  I began by questioning the assumption that the most persistent difficulties sit in the algorithms themselves. From my experience, that is rarely the case. I then looked at governance and how I often see it framed as a barrier rather than the structure it is intended to provide. From there I explored the model lifecycle, and how fragmentation across that lifecycle introduces friction that only becomes visible later on.  More recently I focused on explainability and fairness, not as compliance requirements, but as design choices.

In earlier articles in this series, I’ve been trying to reframe the way modelling challenges are usually understood.  I began by questioning the assumption that the most persistent problems sit in the models themselves. In my experience, that rarely holds. I then looked at governance, and how I often see it applied too late to provide meaningful structure. From there, I explored the model lifecycle and how fragmentation across that lifecycle creates friction that only becomes visible downstream. In the previous article, I focused on explainability as a design choice rather than a validation task. The central idea was that understanding needs to be preserved as a model moves through the organisation, not reconstructed at the end. 

Explainability almost always comes up at some point in a modelling conversation. In my experience it tends to arrive late, during validation, or in response to challenge from risk teams or regulators. It is treated as something we must demonstrate after the model has already been built. At that stage the question becomes, can the model be explained. In practice that is often the wrong question.  In earlier articles in this series I argued that many of the challenges organisations face do not originate in the models themselves, but in how models are developed, governed and carried through the organisation. I also explored how fragmentation across the model lifecycle creates friction that only becomes visible later. 

In the first two articles in this series, I challenged a couple of assumptions that often shape discussions about modelling in banking. 

The first was that the most persistent problems rarely sit within the algorithms themselves. The second was that governance, when properly designed and applied, is not the obstacle it is sometimes perceived to be.  Taken together, these ideas lead to a fairly obvious question.

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.

After more than 30 years working in credit risk modelling and leading Paragon Business Solutions through much of that journey, one thing has become increasingly clear to me.  The biggest challenges in modelling are rarely caused by the algorithms.