What happens if banks delay updating their model risk governance for SR 26-2?

Why delaying SR 26-2 model risk governance creates a widening gap

SR 26-2 pushes banks to treat model risk governance more proportionately, which means the delay is not just administrative. Institutions that wait keep applying high-friction review to low-risk use cases, so approvals stay slow and scarce expert attention is consumed by routine oversight instead of material risk decisions.

That creates a compounding operating-model gap: faster peers can build an AI governance operating model that routes low-risk cases through lighter controls, while slower banks keep tying throughput to the most conservative review path. The result is not only higher cost, but less room to scale AI use cases without adding review headcount.

A second-order effect is competitive, because governance latency starts shaping deployment velocity. When model approvals lag, product teams work around the control process, implementation teams defer automation, and the institution loses time-to-value on the same capabilities peers are deploying with less friction.

What changes operationally when all models are treated the same

The core mistake is collapsing materially different model types into one approval standard. If every model receives the same expert review, the governance function spends the most expensive oversight time on the least risky work, which is an inefficient use of scarce validation, legal, compliance, and risk capacity.

That is where the GRC maturity model is useful as a planning lens, because the practical shift is from ad hoc, blanket review toward risk-tiered governance with clearer ownership and faster decision paths. Banks that do this well separate review intensity from the mere existence of a model.

Done badly, the organisation also ends up with unclear accountability. Business teams may see governance as a blocker, risk teams may see business pressure as a reason to waive controls, and neither side gets a stable operating rule for what must be escalated versus what can be handled under delegated oversight.

Why slower governance becomes a strategic disadvantage

Slow governance is not only an internal process issue. It reduces the bank’s ability to experiment, learn, and redeploy capacity into higher-value AI and analytics work, which matters when peers are already using proportionate controls to move faster.

For banks with broader identity and AI operating model work underway, an identity security programme can help align governance, ownership, and role clarity across the teams that approve, build, and operate models. The practical benefit is not abstract maturity, but fewer handoff delays and better control over who can approve what, when, and under which conditions.

If the bank delays, the gap tends to widen over time because the competitors do not just move faster once. They also accumulate better operating data, stronger automation patterns, and more confidence in risk-tiered governance, which makes later catch-up harder.

Risk and Threat Considerations

Delayed governance creates exposure to control drift, where teams use workarounds to avoid slow approvals and those exceptions become the de facto operating model. The longer that continues, the harder it becomes to distinguish legitimate low-risk automation from weakly governed model deployment.

Failure mechanism: A one-size-fits-all governance process forces low-risk models through the same high-friction review path as higher-risk ones, so the organisation accumulates bottlenecks, informal exceptions, and inconsistent decision-making.

Impact: The bank loses deployment speed and cost efficiency, while governance credibility weakens because teams experience the control as delay rather than risk management.

Practitioner Guidance

What to prioritise: Separate model governance by risk tier first, not by organisational habit. If every use case still needs the same approvers and same evidence set, the operating model has not actually changed.

What to verify: Check whether low-risk models can move through lighter review without reducing accountability, and whether the approval path is measurable enough to show where time is being spent. If not, the bank will struggle to prove that governance is proportionate rather than simply slower.

Common mistake: Treating SR 26-2 as a documentation exercise instead of a throughput problem. The real test is whether governance frees expert capacity for the material cases that need deeper challenge.

Practitioner takeaway: The winning design is not weaker governance, but sharper governance, where review intensity matches actual risk and does not tax the institution’s ability to scale.

Related resources from NHI Mgmt Group

• How should banks redesign model governance under SR 26-2 without losing control over higher-risk models?
• How does the consumer-secret-entitlement model help with governance at scale?
• What happens when banks expand digital services without matching analytics and governance to the new risk profile?
• Why do banks still face supervisory risk if generative AI is outside SR 26-2?