When Senior Judgment Overrides the Model Every Time: The Confidence Problem in Credit Decisions

The Comfort of Judgment When Certainty Is Elusive

Financial services institutions operate in an environment where decisions carry lasting consequences. Approve the wrong credit, and losses compound over years. Reject a sound opportunity, and revenue is forgone. Tighten standards too early, and growth stalls. Loosen them too late, and risk accumulates silently.

In this context, experience is not just valued—it is revered. Senior underwriters have seen economic cycles, portfolio deteriorations, and recovery patterns that models trained on recent data cannot fully capture. They have lived through credit events that taught lessons no algorithm can replicate.

When a significant credit decision reaches the approval stage, it is their judgment that leadership trusts most. The model provides a recommendation. The data is comprehensive. The risk score is calculated. But when the credit committee convenes, the real deliberation begins—not about what the data says, but about whether the data is capturing what truly matters.

And more often than not, the decision is influenced less by the model's output and more by the instincts of those who have underwritten similar credits before.

This is not irrationality. It is a deeply human response to uncertainty. But it is also a form of decision-making that quietly erodes institutional clarity—because when experience consistently overrides evidence, the organization loses the ability to distinguish between situations where judgment is genuinely superior and situations where it is simply more comfortable.

Why Experience Feels Safer Than Evidence

Credit decisions are made under conditions of irreducible uncertainty. No model can perfectly predict borrower behavior. No dataset fully captures macroeconomic shifts, management quality, or competitive dynamics that will determine repayment capacity years from now.

In this environment, the model provides precision—a score, a probability estimate, a risk-adjusted recommendation. But precision is not the same as certainty. And when senior decision-makers recognize the gap between what the model can measure and what actually determines credit performance, they revert to judgment informed by experience.

This feels rational. The experienced underwriter remembers the case ten years ago that looked similar—and defaulted. They recall the sector that appeared stable—until regulatory change triggered a wave of bankruptcies. They have pattern recognition that no model trained on the most recent five years can replicate.

But relying on experience also introduces bias. Memories are selective. Recent events carry disproportionate weight. Anecdotes are vivid, but not necessarily representative. And when experience becomes the primary decision input, the institution struggles to articulate why one credit was approved and another was not—beyond "the committee felt more comfortable with this one."

The model offered evidence. Experience offered confidence. And in high-stakes decisions, confidence often wins.

The Financial Services Reality: Data That Informs But Does Not Decide

Many financial services leaders will recognize this pattern:

A credit application reaches the approval committee. The applicant scores well on quantitative metrics. Financial statements are solid. Bureau data shows no red flags. Industry outlook is stable. The model recommends approval within standard terms.

But during the committee meeting, concerns surface. A senior underwriter notes that the applicant's primary customer is experiencing margin pressure. Another recalls that a similar case approved two years ago is now showing early signs of stress. The CFO questions whether the sector outlook assumptions embedded in the model are still valid given recent regulatory signals.

Each concern is legitimate. Each reflects experience that the model does not explicitly account for. And collectively, they shift the decision from approval to deferral—pending additional analysis, tighter covenants, or a higher pricing adjustment than the model recommended.

The model was not wrong. But it was not trusted. And the decision ultimately rested not on what the data said, but on whether the committee felt comfortable proceeding despite residual uncertainties the data could not resolve.

This is the central tension: the institution has invested heavily in credit analytics, yet senior judgment remains the decisive factor. The models provide inputs. But they do not provide the confidence required to act.

How Intuition Quietly Overrides Data Without People Realizing It

One of the most subtle dynamics in experience-driven credit decisions is that the override often does not present itself as an override. It presents itself as prudent risk management.

The model says approve. The committee says, "We should approve—but let's add these three additional covenants, increase the guarantee requirement, and price 50 basis points higher than the model recommends."

From the committee's perspective, this is not rejecting the model. It is refining it. Incorporating context the model cannot see. Applying judgment where the data is incomplete.

But from an institutional perspective, the effect is the same: the model's recommendation was not followed. The decision was shaped primarily by subjective assessment, informed by experience and pattern recognition that cannot be documented, tested, or replicated.

Over time, this creates ambiguity about what role the model actually plays. Is it a decision tool—or is it a compliance artifact that generates a number the institution must reference but does not genuinely rely on?

When overrides become routine, the organization loses clarity about whether it is data-driven or judgment-driven. It has data. It references data. But the actual decisions are made through a parallel process that draws heavily on instinct, precedent, and institutional memory.

When Accountability Becomes Diffused Behind Experience

Another consequence of experience-driven decision-making is the diffusion of accountability. When a credit decision goes wrong, the post-mortem often reveals that everyone followed process, consulted the model, and acted within their mandate—but no one can clearly articulate why the decision felt right at the time.

The model provided a score. The committee applied judgment. The decision was collective. And when the credit deteriorates, responsibility dissolves into process: "We followed the framework. The model was consulted. The committee voted. We made the best decision we could with the information available."

But the lack of clarity about why the decision was made—beyond "experienced underwriters felt it was acceptable"—makes it difficult to learn systematically from the outcome. Was the model insufficient? Was the judgment flawed? Was the context misread?

Without a clear rationale grounded in evidence, the institution cannot confidently say which decisions it would make differently in the future—and which it would make the same way again.

This is the hidden cost of relying too heavily on experience: decisions are made, but understanding is not built. Outcomes occur, but institutional learning does not follow.

The Hidden Patterns Across Industries

While the operational context differs, the pattern of experience overriding evidence appears across sectors:

In retail and e-commerce, marketing teams repeat strategies that worked historically, even when current data suggests customer behavior and competitive dynamics have shifted. The precedent feels safer than adaptation.

In manufacturing, experienced operators fix recurring problems with proven workarounds, while root cause data sits unexamined. The fix is immediate and familiar. The investigation feels slower and riskier.

In financial services, credit committees debate decisions already supported by models, adding subjective adjustments based on pattern recognition that cannot be formalized. The model provides a starting point. Experience determines the final answer.

The underlying dynamic is consistent: when uncertainty is high and consequences are lasting, organizations default to the confidence that comes from experience—even when evidence suggests a different path.

What Leaders Should Be Asking

If this dynamic feels familiar, it may be time to examine not whether experience is valuable, but whether it is being used to complement evidence or replace it:

• How often do we override model recommendations—and can we clearly explain why in each case?
• When we add covenants, adjust pricing, or defer decisions beyond what the model suggests, are we correcting for genuine gaps in the data—or are we simply hedging against our own discomfort?
• If a decision succeeds, can we articulate whether it was because the model was right, the judgment was right, or we were fortunate?
• Are our most experienced underwriters teaching the next generation how to make decisions—or are they teaching them what to decide based on precedent?

These questions shift the focus from defending experience to understanding its role. They acknowledge that judgment is essential—but only when it is transparent, testable, and capable of being refined through evidence.

Why Awareness Must Precede Solutions

This is not an argument against experienced judgment or senior underwriter discretion. Credit decisions involve complexities that models cannot fully capture. Human judgment will always be necessary.

But judgment becomes problematic when it is used to avoid relying on evidence rather than to interpret it. When the response to a model recommendation is reflexive skepticism—when the instinct is always to adjust, layer in safeguards, or defer—then experience is not enhancing decision-making. It is substituting for it.

For financial services leaders managing regulatory scrutiny, competitive pressure, and portfolio performance expectations, this distinction is not theoretical. Decisions that cannot be explained erode institutional trust. Overrides that are routine but undocumented create audit risk. Credit frameworks that exist on paper but are not genuinely followed undermine the credibility of the institution's risk management.

Clarity does not come from eliminating judgment. It comes from making judgment transparent—articulating when and why experience should override evidence, and creating institutional memory that allows those overrides to be evaluated, refined, and improved over time.

A Question for Leaders

If your credit committee were asked today: "Which decisions are we making based on evidence—and which are we making based on the fact that experienced underwriters have seen something like this before?"—could you draw a clear line between the two?

Experience tells you what patterns to look for. Evidence tells you whether those patterns are present now. And in credit decision-making, where both risk and opportunity are determined by the quality of judgment under uncertainty, the difference between the two is not just operational—it is existential.

What credit decision did your institution make this quarter because it felt right—and could you explain, with evidence, why you would make it the same way again?

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