How AI Can Help Banks and Credit Unions Defend Against Credit Card Fraud
By Nicole Volpe, Contributor at The Financial Brand
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As AI intensifies cyber risk across financial services, credit card fraud has become a high-stakes, high-volume proving ground for cyber defense.
Credit card fraud matters because it presents a direct path to financial loss, moves at transaction speed, and forces institutions to make consequential decisions in milliseconds: approve, decline, or investigate. The stakes are further raised for financial institutions because credit card fraud puts the customer experience directly on the line: every fraud decision the issuer makes risks either allowing a bad transaction through or blocking a legitimate one.
At the same time, the data-rich environment that has expanded the attack surface for AI-driven card fraud is equally well suited to AI-enabled defenses. While transaction and behavior data, and device and network data, give fraudsters more to work with, they also give institutions more signals to analyze as risk patterns emerge.
No doubt, the global AI arms race might seem beyond the reach of many smaller financial institutions, but banks and credit unions may be better positioned to protect themselves than many assume. To do so, they must first take stock of what these shifts mean for their own card programs: what is driving the growth in fraud, why traditional detection methods are under strain, and how AI is reshaping defenses.
Want more insights like this? Check out Elan’s content portal: Credit Card Issuance: Strategies & Solutions
The Growing Threat
For decades, credit card fraud prevention was built around detection of a relatively well-established set of occurrences: stolen cards, cloned cards, suspicious purchases, or otherwise unusual transactions. While those risks persist, the center of gravity has shifted from physical theft to digital and identity-based attacks. Globally, payment card fraud losses totaled $33.41 billion in 2024 and are projected to rise to $41.06 billion by 2030 and $48.50 billion by 2034.
That shift reflects the broader movement of payments into digital channels. As e-commerce, mobile payments, and card-not-present transactions have grown, fraudsters have found more opportunities in environments where physical card verification is not possible. A new analysis of Federal Reserve data found that card-not-present fraud rates for non-prepaid debit cards continued to climb through 2023. And FICO projects CNP fraud globally to hit $49 billion by 2030.
A new white paper from Elan Credit Card offers a deep dive into the changing state of play, highlighting the increasing complexity of fraudsters’ methods. New account fraud, for example, has grown as AI makes synthetic identities easier to generate and test. AI automation similarly brings scale to account takeover fraud, through which bad actors generate unauthorized transactions, change account details, and hunt for additional fraud pathways.
Phishing and social engineering have also gained scale, as AI automates outreach to victims and enables faster harvesting of the personal information needed to gain their trust. And AI has accelerated exploitation of breached troves of card data and related credential and authentication hacking.
“Today’s fraud environment moves faster, and it exploits the industry’s expanding and diverse range of databases to a greater degree,” according to Mitch Pangretic, SVP, Director of Strategic Partnerships at Elan Credit Card. “The upshot is that it’s harder to contain using current approaches.”
The Limits of Traditional Fraud Detection
Most financial institutions have long relied on a combination of rule-based systems and statistical models to detect suspicious card activity. The former rely on predefined thresholds and triggers, flagging transactions, for example, that exceed a certain dollar amount, occur in an unusual location, or follow a pattern that appears inconsistent with the cardholder’s normal behavior. Statistical models add more flexibility by analyzing historical transaction data and estimating the likelihood that a transaction is fraudulent.
“While these approaches helped create the foundation of modern fraud detection, Pangretic said, “they were designed for a different environment—one with lower transaction volume, more predictable fraud patterns, and fewer digital attack surfaces.”
Their first limitation is that they are often reactive. Rules must be developed based on verified fraud incidents, then tested and maintained; and statistical models depend heavily on historical data. But such approaches fall short when fraud tactics change quickly and no longer reflect known patterns.
Their second limitation is false positives. A cardholder traveling, making a large purchase, or changing spending habits may trigger a fraud alert even when the transaction is legitimate. These interruptions may protect against losses, but they can also frustrate customers and members. An unusual transaction may happen because the consumer is in a moment of stress already, needing a new laptop in the middle of a workday deadline, for instance. A declined transaction at the wrong moment can feel less like protection and more like institutional failure.
Their third limitation is operational burden. As transaction volumes grow and fraud patterns become more complex, maintaining rule sets and reviewing suspicious activity require more staff time and technical capacity. This is especially challenging for smaller banks and credit unions, which must manage the same category of threat as larger issuers without the same internal scale.
How AI Is Transforming Detection
As the effectiveness of traditional defenses diminishes over time, AI is filling the gap. “To prevent fraud today, we need systems that can adapt in real time, incorporate even more signals, and better distinguish real risk from ordinary changes in customer behavior,” said Pangretic.
The most immediate application of AI is real-time decisioning. AI tools can evaluate a transaction as it happens and recommend whether to approve, decline, or flag it for review. These systems can weigh more signals than traditional rules-based approaches, including transaction history, device behavior, location data, spending context, and patterns across similar activity.
Adaptability is equally important. Fraudsters continually test controls and adjust their methods. AI models can incorporate new data and refine decision-making over time, reducing dependence on manual rule updates. This does not eliminate the need for oversight, but it changes the pace at which institutions can respond and expand their defensive arsenals.
AI also changes how fraud work is distributed. High-volume, lower-risk reviews can be automated or prioritized more efficiently, allowing fraud analysts to focus on more complex cases. For financial institutions, this can reduce operational strain while improving the consistency of fraud decisions.
In the future, according to the Elan white paper, the industry is focused on leveraging new fraud-prevention capabilities that also reduce friction in the user experience.
- Behavioral biometrics can help identify users based on how they interact with devices, not just what credentials they enter. Typing cadence, navigation patterns, device handling, and session behavior can all help detect account takeovers in progress without interrupting legitimate users unnecessarily.
- Federated learning offers a way for institutions to improve fraud models collaboratively without sharing raw customer data. That could be especially important in a heavily regulated environment where privacy and data control remain central concerns.
- Graph-based modeling can identify hidden relationships among accounts, devices, transactions, merchants, and identities. This matters because organized fraud often appears less suspicious when each event is viewed alone. AI-enhanced analysis can reveal patterns within complex, high-frequency data sets that isolated transaction monitoring may miss.
- Adaptive authentication can also reduce unnecessary friction. Rather than treating every transaction or login the same way, institutions can apply stronger verification only when risk signals justify it. Low-risk activity can proceed with minimal disruption, while higher-risk activity triggers added scrutiny.
Taken together, these capabilities suggest that fraud detection is moving from a rules-and-alerts model toward a more contextual model. “One of the key advantages AI brings to the problem of fraud detection is its ability to understand behaviors in context,” said Pangretic. “Its ability to parse relevant signal can be a game-changer because it enables active fraud detection without disrupting legitimate transactors.”
A fraud system that stops bad transactions but regularly blocks good ones can damage trust. Conversely, a system that is too permissive can expose cardholders and institutions to losses. The strategic challenge is balancing protection, precision, speed, and user experience.
Next Steps for Banks and Credit Unions
Financial institutions are already moving toward AI-enabled fraud detection as card fraud becomes faster, more digital, and more adaptive. In KPMG’s 2025 report on banking technology, which included both large and small institutions, 89% of respondents said they were increasing their budget for addressing cyber risk. More than 90% said they had active pilot, proof-of-concept, or live fraud use cases under way that involve AI.
Smaller banks and credit unions looking to add AI capabilities to their credit card fraud risk can begin by assessing their financial resources and operating capacity. Advanced fraud detection requires ongoing investment in infrastructure and specialized expertise. For some institutions, building and maintaining those capabilities internally may not be practical, which is why many are evaluating technology, network, and platform partners that can provide such infrastructure and support in the context of their other services.
At the same time, because AI-driven fraud detection depends on robust access to customer and member data, institutions must account for privacy, consent, and regulatory compliance as part of deployment. Fewer than half of respondents to KPMG’s survey considered themselves sufficiently prepared to navigate regulatory uncertainty related to data and cybersecurity. Frameworks such as General Data Protection Regulation (GDPR) and Payment Card Industry Security Standards Council (PCI DSS) impose strict requirements on how that data is collected, stored, and used.
Ultimately, a resilient fraud strategy depends on how well institutions align technology, operational capacity, and user experience. Stronger controls cannot come at the expense of unnecessary cardholder disruption, and better user experience cannot come at the expense of weaker protection. The institutions best positioned for the next phase of card fraud will be those that can bring those priorities together—whether internally or through the right partner.
