AI discussions in financial services often frame explainability and accuracy as opposing goals.
That framing is misleading.
The real question is not whether a model is maximally accurate, but whether it is accurate enough while remaining governable.
Why accuracy alone is not sufficient
Highly complex models may outperform simpler ones on benchmarks, but:
- they are harder to validate
- they are harder to monitor
- they are harder to explain under stress
In regulated environments, these costs matter.
What regulators care about more than accuracy
Regulators prioritize:
- consistency
- stability
- transparency
- accountability
A slightly less accurate model that can be explained and defended often carries less risk.
When simpler models outperform in practice
In many risk contexts:
- explainable models are easier to tune
- issues are detected earlier
- trust improves across teams
Operational effectiveness often outweighs marginal accuracy gains.
Making the tradeoff intentionally
Strong institutions:
- assess accuracy relative to risk impact
- document tradeoffs explicitly
- align model choice with use case criticality
This turns tradeoffs into governance decisions, not technical arguments.
Read next:
→ Explainable AI in Financial Services
