When AI Gets It Wrong: Why Exception Handling Is The New Finance Discipline

Sunil Padiyar is the Chief Technology Officer (CTO) of Trintech, a global leader in AI Financial Close solutions.

getty​AI in finance is moving quickly from helping people make decisions to taking action within financial processes. That brings a different kind of risk into the picture.

A mistake in an AI-generated summary can often be caught before it goes anywhere. The stakes change when an AI agent is classifying transactions, investigating variances or resolving exceptions. At that point, an incorrect decision can become part of the financial process before anyone has reviewed it.

Finance leaders have spent decades building controls around human error and traditional automation. AI adds a new challenge because the system can follow its permissions, produce a reasonable-looking result and still reach the wrong conclusion. As organizations give AI more responsibility, they need a more disciplined approach to recognizing and managing those moments.

Exceptions are nothing new in finance. Unmatched transactions, unexpected balances and unusual journal entries already trigger additional review. What is different with AI is where the error can originate and how quickly it can spread.

Models can produce false positives. Performance can drift as business conditions and data change. An agent working with incomplete context can make a decision that appears reasonable but turns out to be wrong.

Consider an AI system helping resolve transaction-matching exceptions. A change in source data or an unfamiliar transaction pattern could lead the system to repeatedly apply the wrong classification. One incorrect match may be easy to identify. The larger concern is the same decision being repeated across hundreds or thousands of transactions before someone recognizes the pattern.

Automation changes the scale of the problem.

This is where I believe finance leaders need to rethink what good AI performance looks like. The goal cannot simply be maximizing the number of decisions AI can make on its own. It should be increasing automation while making it easier to recognize when the system is uncertain, wrong or needs human judgment.

This is why accuracy alone is an incomplete measure of AI performance in finance. A system can maintain a high overall accuracy rate while still producing a small number of financially significant or difficult-to-detect errors. Finance leaders also need to understand how quickly problems surface, how far they can travel and what controls exist to contain them.

Traditional exception management focuses primarily on the process: Which transactions did not follow the expected path, and what needs to happen next?

AI introduces another layer. Finance teams need visibility not only into the exception itself but also into how the system arrived there.

If an agent investigates a variance and determines that no further action is necessary, a reviewer needs more than an alert if that conclusion proves incorrect. They need enough context to understand what information the agent considered, what it concluded and what action it took.

Not every exception should receive the same response. A small discrepancy in a routine process is different from an unusual journal entry or material variance. Organizations need clear thresholds for when AI can continue working and when human judgment should take over.

The process should also extend beyond fixing the immediate issue. Recurring exceptions can expose weaknesses in data, models, workflows or controls. The exception itself becomes useful data. In an agentic environment, the goal should not simply be to route an exception to the right person, but to use the pattern of exceptions to improve how the system behaves over time. The cycle becomes detect, explain, contain, resolve and learn.

One of the promises of AI is that finance professionals can spend less time reviewing routine work. That value disappears if every automated decision requires the same level of human validation. Human oversight is more useful when it focuses on uncertainty, materiality and risk.

A high-confidence decision involving a routine transaction may require little intervention. A decision involving incomplete information, an unusual pattern or a material amount may warrant review.

This is where governance and automation should reinforce each other. Clear escalation paths, audit trails and defined boundaries can help finance teams understand when human involvement is necessary without forcing people to supervise every action.

Finance transformation has traditionally been measured through automation rates, processing times and reductions in manual effort. Those measures still matter, but they do not tell finance leaders how an AI system behaves when something goes wrong.

How quickly was the exception detected? Was its impact contained? Could the finance team understand what happened? Did the issue reach the right person?

Those questions provide another measure of AI maturity.

As AI systems become more autonomous, this principle extends beyond finance. Technology leaders need to design for failure as deliberately as they design for successful automation. The ability to recognize uncertainty, contain an incorrect action and bring a person back into the process will increasingly determine whether autonomous systems can be trusted with consequential work.

No complex financial environment will eliminate exceptions entirely, nor should that be the standard for AI. The more important question is whether those exceptions are visible, explainable and manageable.

As AI takes on more of the work behind finance, exception handling cannot remain an edge case considered after automation has been deployed. It needs to be part of the architecture from the beginning.

The real test of AI in finance is not only what happens when the technology gets the answer right. It is whether finance remains in control when it gets the answer wrong.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?