For fintechs and banks alike, models are at the heart of decision-making — from credit scoring and fraud detection to pricing, capital allocation, and even AI-powered chatbots. Regulators know this, which is why model risk management has become a major area of scrutiny under supervisory guidance like SR 11-7.

Yet despite the stakes, many firms stumble when it comes to model validation. Over the years, I’ve seen a familiar pattern: the same mistakes repeated across different organizations, often leading to the same costly consequences.

Here are the three most common pitfalls fintechs face in model validation — and what you can do to avoid them.

1. Treating Validation as a Checkbox Exercise

Too often, validation is approached as a compliance formality rather than a rigorous review. The validation team (sometimes even internal staff with limited independence) writes a brief memo, checks a few boxes, and moves on.

The problem? Auditors and regulators can see through this immediately. A “lightweight” validation leaves your institution exposed — not only to compliance findings, but to real financial and reputational risk if the model behaves unexpectedly.

How to avoid it:

• Ensure validation is conducted independently of the model development team.
• Go beyond surface-level testing: challenge the underlying assumptions, data sources, and conceptual soundness.
• Document not just the tests performed, but also the rationale for why those tests are sufficient.

2. Weak Documentation That Won’t Hold Up Under Scrutiny

Model documentation is often treated as an afterthought — something thrown together when the validation deadline approaches. The result is gaps, inconsistencies, or vague language that might satisfy internal stakeholders but doesn’t survive regulatory review.

I’ve seen cases where a firm had a technically sound model, but because the documentation was sloppy, the entire model program was flagged as deficient.

How to avoid it:

• Treat documentation as a living record, not a one-time deliverable.
• Include model purpose, assumptions, data lineage, methodology, testing, and limitations.
• Write with the assumption that someone outside your team (an examiner, auditor, or new employee) should be able to fully understand the model.

3. Neglecting Ongoing Monitoring

Validation isn’t “one and done.” A model that works well at launch can degrade quickly if the data environment changes — think credit models during COVID, or fraud models as new attack vectors emerge.

Unfortunately, many organizations skip systematic monitoring, assuming the annual validation will catch any issues. By then, the damage may already be done.

How to avoid it:

• Establish a clear model monitoring plan with defined metrics, thresholds, and reporting frequency.
• Assign ownership for monitoring (not just development or validation).
• Treat monitoring results as an early warning system, feeding back into governance and, if necessary, triggering re-validation.

The Bottom Line

Model validation is more than just a regulatory requirement — it’s a safeguard for the integrity of your entire business. By avoiding these three mistakes — treating validation as a checkbox, underestimating documentation, and neglecting monitoring — you’ll not only reduce compliance risk, but also build stronger, more resilient models.

If you’d like to go deeper, I’ve put together a Model Documentation Checklist that highlights the core practices every institution should cover. It’s a simple resource you can use to make sure your validation program is audit-ready.