Photo By: Andre Taissin
Artificial intelligence is moving deeper into banking, promising faster decisions, lower costs and a financial system capable of processing more information than any human workforce could manage alone. But as banks race to automate everything from fraud detection to lending decisions, a less glamorous question is becoming increasingly important: What happens when the machine is wrong?
Rahul Naithani, Field CTO, Banking and Financial Services at NewRocket, argues that the industry’s answer may be less reassuring than it sounds.
““Human-in-the-loop” has become enterprise tech’s favorite “sugar-free” label. It gets ceremonial mention in AI pitch decks to make something complex sound safe, clean, and fully under control.”
The observation gets to the heart of a growing problem in financial services. Simply putting a person somewhere in an AI workflow does not necessarily create meaningful human oversight. If an employee receives an algorithmic recommendation without the information needed to understand it, has seconds to respond, or lacks the authority to override it, the human is not really exercising judgment. They are approving a machine.
That distinction matters enormously in banking.
Financial decisions routinely involve circumstances that do not fit neatly into historical data. A customer may have an unusual income pattern because they have recently started a business. A transaction may look suspicious because someone is traveling. A borrower may have experienced a temporary disruption that makes their financial profile look worse than their underlying circumstances suggest.
AI systems can identify patterns across millions of transactions and accounts. They can flag anomalies, summarize documents and generate recommendations at a speed that humans cannot match. But recognizing an unusual pattern is not the same thing as understanding why it exists.
This is where deliberate architecture becomes critical.
A bank designing an AI-assisted decision system has choices to make at every stage. What information does the employee see? Which evidence is shown alongside the model’s recommendation? How confident is the system? Can the employee interrogate the reasoning or request additional information? How much time is allocated for review? And, perhaps most importantly, can the employee actually change the outcome?
A human reviewer who has context, time and authority can serve as a genuine control. A reviewer who has none of those things can become little more than a rubber stamp.
The temptation to automate that distinction away is understandable. Banks operate at enormous scale, and efficiency is a powerful incentive. If an AI model can review thousands of cases while humans examine only the exceptions, organizations can potentially redirect employees toward situations where judgment adds the most value.
But that only works if the exceptions are designed intelligently.
Consider fraud detection. An algorithm might flag a transaction because it deviates sharply from a customer’s normal behavior. A human investigator can potentially determine whether the deviation reflects fraud, a legitimate purchase, a family emergency or a change in circumstances. Yet that investigation requires access to relevant account history and other evidence, sufficient time to examine it, and authority to clear the transaction when the evidence supports doing so.
Without those elements, “human review” risks becoming procedural theater — precisely the kind of symbolic safeguard Naithani’s critique points toward.
The same principle applies to lending. An AI system might produce a credit assessment based on a vast collection of financial signals. A loan officer can add valuable context, but only if the system allows that context to matter. If organizational policy effectively requires employees to follow the model except in extraordinary circumstances, the human becomes an intermediary rather than a decision-maker.
This does not mean banks should reject AI. Quite the opposite. The technology may be most valuable when it is designed to augment professional judgment rather than quietly replace it.
That requires banks to treat the surrounding workflow as seriously as the model itself.
The architecture of an AI system should therefore include not only data pipelines, models and computing infrastructure, but also decision rights. There should be clear rules about when a human must intervene, what information they receive, how uncertainty is communicated, what questions they can ask, and when they are empowered to disagree.
There is also a cultural dimension. Employees need to know that challenging an algorithm is legitimate rather than an act of noncompliance. Managers need mechanisms for identifying repeated disagreements between humans and models. And risk teams need records showing not simply what the AI recommended, but what the human reviewer saw, decided and why.
The goal is not to put a human in front of every automated decision. That would defeat much of the technology’s purpose. The goal is to ensure that where human judgment is required, the human is equipped to exercise it meaningfully.
As AI becomes more embedded in banking, that may prove to be one of the industry’s defining design challenges. The most consequential question will not simply be how intelligent the model is.
It will be whether the system around it allows a person to be intelligent too.
