Whyexplainabilitymattersmoreinfinancethanelsewhere
In most applications, a system that can't explain its own reasoning is a minor inconvenience — a recommendation engine that suggests a product for unclear reasons rarely causes real harm, and the cost of being wrong is usually just a bad suggestion. Finance doesn't offer that margin. A model that concludes something without a visible path to how it got there isn't just harder to trust; it's a liability, to the person relying on it and to whoever eventually has to answer for the decision it informed.
This is what separates explainability from a nice-to-have feature: it's a precondition for responsible use, not an enhancement layered on afterward. A risk manager who acts on a system's output needs to be able to say, credibly, why the action was taken — not "the model flagged it," but which signals mattered, how they were weighted, and what would have changed the conclusion. Without that, human accountability becomes fiction: a person can be nominally "in the loop" while having no real ability to evaluate what they're approving. Building for explainability from the start, rather than trying to reverse-engineer it later, is the difference between a system that supports judgment and one that just launders it.