AI can detect financial fraud more efficiently than previous technology did, but it also flags legitimate transactions that it shouldn't. CardMapr.nl on Unsplash, CC BYImagine you’re at the supermarket checkout. Your cart is full. The line behind you is long. You tap your card. Declined.
You try again. Declined.
You haven’t overspent. You haven’t done anything suspicious. But somewhere inside your bank’s computer systems, a machine made a decision about you in less time than it takes to blink – and it made a mistake.
What just happened? And why does it keep happening to people who haven’t done anything wrong?
This isn’t a rare glitch, but something that happens to millions of people every day. And most of us have no idea why it happens or what we can do about it. The answer lies inside a fraud detection system powered by AI.
As a data science teaching professor
and former financial-services data scientist, I understand how this system works and can explain why it sometimes fails the very customers it’s meant to protect. Just as important, I can help you find out what you need to know and what you can do if you or your loved ones are unfairly flagged.
A decision in milliseconds
When you tap your card, a signal travels to your bank’s fraud detection system in the time it takes to blink. The transaction processing at your checkout is fully automated, operating within AI systems that handle millions of payments simultaneously, and computes a risk score based on dozens of features extracted from that single moment. Those features might include the transaction amount relative to your recent spending average; the type of merchant; your geographic location; the time of day; the device used for online purchases; and how this purchase compares to your historical patterns.
Once those factors are plugged in, an algorithm scores your purchase in real time. A model trained on millions of past transactions then assigns each combination of features a probability on how likely it is that this transaction would be fraudulent. If that probability crosses a threshold, the transaction is blocked or flagged for review. The whole process takes less than 200 milliseconds.
‘99% accurate’ still fails millions of people
What sets this technology apart is speed. Financial institutions process millions of transactions every day, which is far greater than any human team can effectively monitor. Banks also have fraud analysts, but their work happens at a different layer entirely – reviewing patterns, investigating cases, and handling disputes that the automated system escalates to them.
To their credit, these new systems are usually accurate at catching fraud. Banks lose far less money due to card fraud today than they did before machine learning – one of the foundational technologies that power today’s AI systems – became standard.
Still, the word “accurate” conceals a problem. Consider the numbers. The Federal Trade Commission reported that Americans lost more than $12.5 billion to fraud in 2024 – a 25% increase from the year before. As banks process more transactions than ever, fraudsters are keeping pace, too.
And here is the part that is especially worth noting: According to Stripe, one of the world’s largest payment processors, “false declines” (legitimate transactions wrongly rejected) are a structural problem across the entire industry, and industry research consistently suggests they cost the financial system more than actual fraud does.
These errors aren’t random. They cluster around people and situations that the algorithm wasn’t properly trained to expect. Buying gas in a city you’ve never visited or making a large rent payment for the first time aren’t inherently suspicious. But to a machine trained on past patterns, they can look that way.
There’s something even more troubling. These algorithms learn from historical data, which is almost always imbalanced. Because fraudulent transactions are rare on a per-transaction basis, the model has seen relatively few examples of what fraud looks like across every type of customer.
What does this mean? Research has found that customers in lower-income areas and communities of color face higher rates of erroneous declines. When a model hasn’t seen enough transactions from a particular group of people or in a given situation, it has less data to build an accurate baseline for them. So when something slightly unusual happens, it flags it. Not out of intent, but out of unfamiliarity.
The model isn’t necessarily explicitly discriminating against anyone. But its outputs can still produce what researchers call disparate impact – unequal harm, distributed unequally.
As researchers at MIT explain in their book “Fairness and Machine Learning,” this is a known limitation. A model trained on incomplete representation will perform less reliably for the groups it saw least. The fix isn’t to blame the algorithm, but to train it on better, more representative data, and to test its error rates across different customer groups before deployment.
When machine learning declines a payment, you’re faced with a black box that isn’t designed for human interpretation.
Vitaly Gariev on Unsplash, CC BY
Why you don’t have the right to an explanation
What makes these cases worse is the lack of any information.
When a loan officer denies your mortgage application, the law requires a written explanation. But when an algorithm declines your debit card, you get “flagged by our system” message. If you’re lucky enough to connect with a human representative, they can’t tell you much more.
This gap isn’t an accident. Most high-performing fraud models are black boxes. Their internal logic isn’t designed for human interpretation. A bank may genuinely be unable to articulate plainly why your transaction was stopped. That’s not because it’s hiding something, but because the model itself doesn’t produce a reason. It produces a number.
In response, some financial institutions are moving toward tools that make their algorithms more transparent. Known in the industry as “explainable AI,” these systems are designed to surface the most influential factors behind a given decision – flagging, for instance, that a transaction was blocked because of an unusual location combined with an atypically large amount. It’s a meaningful step toward accountability.
However, these adoptions are uneven, and the explanations that do exist are rarely surfaced to customers.
Meanwhile, those pressures haven’t yet translated into a consistent, enforceable right to a meaningful explanation when your card gets declined. Challenging a decision made by AI can be enormously difficult, and most of us don’t even know we have the right to try.
For most people, the path of least resistance is simply to move on, switch to another card, take their business elsewhere or say nothing. Research suggests a quarter of consumers who experience a false decline never return to that merchant at all.
Some people go further and close the account entirely. That instinct is understandable. However, it carries a hidden cost. A declined transaction won’t appear on your credit report, but closing the card can. Shutting down an account reduces your available credit and can shorten your credit history, which can directly affect your credit score.
What you can actually do right now?
You have more power here than the banks would like you to think.
Call your bank immediately: A fraud flag is probabilistic, not final. A bank representative can override a declined transaction in real time. The model made a guess, but a human can correct it. Do not wait.
Set alerts if you’re planning to make unusual purchases: Most banks allow you to notify them of upcoming travel, large purchases, or changes in your spending pattern. This doesn’t override the model, but it gives it new information to work with, which can prevent the flag from triggering in the first place.
Know your rights: Under the Fair Credit Billing Act, you can dispute erroneous transaction blocks and request an explanation. If you believe you’ve been systematically and unfairly blocked, the Consumer Financial Protection Bureau accepts consumer complaints.
Ask your bank what appeal processes are available: Increasingly, banks are building more customer-facing appeal services. Visa reported 106 million disputes globally in 2025, a 35% rise since 2019, and has called dispute management a “strategic priority.” Improper declines are expensive for payment companies and financial institutions, too, through customer service costs, lost revenue and eroded trust.
The bigger picture
The algorithm that blocked your payment isn’t all-knowing or neutral. It’s a machine making a statistical guess about you, based on data that was probably never perfectly fair to begin with.
As AI spreads further into our daily lives, the question of who controls these decisions, and whether we can challenge them, becomes ever more urgent. The technology will keep expanding into new realms. The rules, and our own financial fluency, need to keep up.
Pragati Awasthi does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.