Picture this: you’re at the checkout line with a full cart, and a queue of impatient shoppers stands behind you. As you tap your card, it gets declined. You try again, only to face the same result. Despite having sufficient funds and no suspicious activity, an error occurred somewhere in your bank’s complex computer systems.
This frustrating experience is far from uncommon. Every day, millions encounter these puzzling declines, often unaware of the cause or solution. The culprit? A fraud detection system driven by artificial intelligence.
Pragati Awasthi, a data science teaching professor and former financial-services data scientist, sheds light on these systems’ operations, highlighting their occasional failures in serving consumers. Here’s what you need to know if you or someone you know falls victim to these erroneous flags.
A Millisecond Decision
Upon tapping your card, a rapid signal is sent to your bank’s fraud detection system. This process, from transaction initiation to decision, occurs in under 200 milliseconds. The system evaluates various features, like transaction amount, merchant type, location, time, and device, to compute a risk score.
AI algorithms analyze these factors in real-time, using historical data from millions of transactions to determine the likelihood of fraud. If the probability exceeds a certain threshold, the transaction is blocked or flagged for further review.
High Accuracy, Yet Millions Affected
The speed and volume at which financial institutions process transactions outpace human capabilities. Although automated systems reduce financial losses from fraud, they still face challenges.
According to the Federal Trade Commission, Americans lost over $12.5 billion to fraud in 2024, a 25% rise from the previous year. Fraudsters keep pace with increasing transaction volumes, and “false declines”—legitimate transactions mistakenly rejected—pose a systemic industry issue, often costing more than actual fraud.
These declines aren’t random. They often occur in scenarios where algorithms weren’t adequately trained to expect deviations, such as buying gas in a new city or making an unusual large payment.
Compounding the issue, these algorithms rely on historical data that is frequently imbalanced. With few examples of fraud across diverse customer types, models struggle to establish accurate baselines, leading to higher erroneous declines for certain demographics, such as lower-income communities and people of color.
This results in a disparate impact, where the harm is not evenly distributed, as noted by researchers.
Lack of Transparency and Explanation
Unlike a mortgage application denial, which requires a written explanation, algorithmic declines provide little information beyond a generic “flagged by our system” message. Even customer service representatives often lack insight into the specific reasons behind a transaction block.
This opacity is due to the “black box” nature of many high-performing fraud models, which aren’t designed for human interpretation. Some institutions are adopting “explainable AI” to highlight key decision factors, like unusual location or transaction size, but these tools are not yet widely used or accessible to consumers.
Without a consistent right to a meaningful explanation, challenging AI decisions remains difficult, leading many to abandon problematic banks or merchants. Research indicates that a quarter of consumers experiencing false declines never return to the merchant.
What You Can Do
Despite these challenges, consumers can take proactive steps:
- Contact your bank swiftly to have a decline reviewed in real time, as human intervention can override AI decisions.
- Set alerts for significant purchases or travel to provide the system with context, potentially preventing false flags.
- Understand your rights under the Fair Credit Billing Act and explore appeal options through your bank or the Consumer Financial Protection Bureau.
As AI continues to influence our financial interactions, understanding and challenging these systems becomes increasingly important, ensuring technology evolves alongside regulatory frameworks and consumer awareness.
