Your declined card may be AI's fault

What you can do when a legitimate purchase is flagged

By: Pregati Awasthi
Drexel University
I The conversation

..... Imagine you're at the supermarket checkout. Your cart is full. The line behind you is log. 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 system, 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 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-service data scientist, i understand how this stem works and can explain why it sometimes fails the very customers it's meant top protect. Just as important, I can help you find out what you need to know and what you can do if your or your loved ones are unfairly flagged.

A decision in milliseconds

..... When you tap your card, a signal travels in the time it takes to blink. The transaction procession 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 for Online purchases; and how this purchase compares to your historical patterns.
..... Once those factors are plugged in, an algorithm scor4es your purchase in real time. A model trained on millions of past transactions then assign 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 millseconds.

'99% accurate still fails millions

..... 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 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 her 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 problems 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't 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.
..... What does this mean? Search has found that customers in lower-income areas and communities of color face higher are 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 unfamiliarly.
..... The model isn't necessarily explicitly discriminating against anyone. But its outputs can still produce what researches call disparate impact - unequal harm, distributed unequally.
..... As researchers at MIT explain in their book, "Fairness and Machine learnings," 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 tot est its error rates across different customers groups before deployment.

No right for 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 a "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 articular plainly why your transaction was stopped. That's not because it;s hiding something, but beaus 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 what do resist 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.
.... 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 credit history, which can directly affect your credit score.

What you can do right now

..... You have more power her than the banks would like yo 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 gives the mode 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 transactions 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 unto our daily lives, the question of who controls these decision, and whether we can challenge them, becomes ever more urgent.

..... The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts. the Conservation is wholly responsible for the content.

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