Fraud in a Credit Card Program? Yes, Here’s What You’ll See

It’s Friday afternoon before a holiday weekend when you get an alert from one of your call centers. They’ve found an “odd” account application. You review the entry. It passed KYC. It passed your fraud risk checks. Everything seems fine. But by Monday morning you have an assortment of similar accounts that are attempting odd purchases, or exhibiting high risk behavior. Yet the only thing they have in common is they applied in all caps. At least that is all you can see from your point of view, and if that is the case, it probably means you don’t have enough fraud tools in place.

Fraud isn't a single problem you solve with a single vendor. It's five or six distinct problems, each with its own playbook, its own economics, and its own blind spots. Confuse them, and you'll end up over-building defenses against the threat you understand and leaving the door open on the one you don't.

Global card fraud losses hit $33.4 billion in 2024 and, per the Nilson Report, are projected to top $400 billion cumulatively over the next decade. And not shockingly, the US absorbing a disproportionate share of it (42% of global card fraud on only 26% of global card volume). It's growing because the tactics keep evolving faster than the controls built to stop them. So before you finalize your terms and features, credit policy, and operations, it's worth walking through exactly what you're up against.

1. First-Party Fraud

First-party fraud is the uncomfortable one, because the "fraudster" is your own accountholder. This is someone who applied honestly, was approved, and now has no intention of repaying you. Or they dispute legitimate charges as unauthorized to avoid the bill. It shows up most commonly as:

• Bust-out fraud — a customer builds a clean payment history for months, then maxes out the line right before disappearing.

• Friendly fraud (first-party chargeback abuse) — a customer recognizes the charge (because they made it!) but files a dispute anyway, treating your chargeback process as a refund shortcut.

• Credit washing — a customer files false claims of identity theft or fraud; disputing legitimate late payments, collections, charge-offs, or defaults, by falsely asserting those debts resulted from fraud or don’t belong to them.

First-party fraud is hard to catch with traditional identity checks, because the applicant’s identity was never the issue. It's a behavioral and underwriting problem, which is exactly why it belongs in your credit policy rather than bolted on as an afterthought. If you haven't yet defined how your program will monitor spend velocity, utilization spikes, and dispute-to-transaction ratios, that's the place to start and we walk through the full framework in how to write a credit policy. We’ve also been testing out some interesting first-party fraud modeling tools that look promising, so there may be more on the horizon on this front.

2. Identity Theft Fraud

This is the fraud most people picture: a criminal uses someone else's stolen personal information: their name, SSN, DOB, address, etc… to open a card the real person never authorized. It typically surfaces as new account fraud at origination, when a fraudster applies using a legitimate identity that isn't theirs.

ID theft fraud is a document- and data-verification problem. Strong identity verification, device fingerprinting, behavioral analytics, and other data checks at the application stage are your first line of defense. It’s also a delivery issue. What policies do you have in place around address verification or change of address internal procedures?

Generally speaking, the earlier you catch it, the cheaper it is. A stolen identity that slips through underwriting costs you the eventual charge-off, which is usually going to be at their full credit limit unless you catch it early enough to shut down the account.

3. Synthetic Identity Fraud

Synthetic identity fraud is when a fraudster combines real data (a legitimate SSN and/or other personal information from a child or someone with thin credit history, for example) with fabricated details (a fake name, invented employment history) to manufacture an identity that doesn't map to any single real victim. No one shows up to dispute the fraud, because no one is being impersonated in a way that triggers alerts. That’s why it’s so dangerous.

This isn't a fringe problem anymore. According to LexisNexis Risk Solutions' 2026 Cybercrime Report, synthetic identity fraud posted an eightfold year-over-year increase and now accounts for roughly 11% of all fraud globally, fueled in part by generative AI tools that make fabricated documents and behavioral patterns far more convincing. Synthetic identities are built to "age" — fraud rings nurture them with real transaction history over months specifically to defeat the models trained to catch bust-out behavior. Detecting them increasingly requires network-level analysis (do multiple "different" applicants share a device, IP, or address pattern?) rather than single-application review.

Fortunately, there are some good tools on the market that are effective at spotting these types of applications. We’ve worked with clients’ on the implementation of these solutions in their operations, but it takes time, testing, and investment to see the benefits.

4. Account Takeover (ATO) Fraud

Account takeover happens after origination: a fraudster gains control of an existing, legitimate cardholder's account, usually through phishing, credential stuffing, SIM-swapping, or social engineering. The fraudster then changes contact details, requests a replacement card, or runs the card up to the available credit before the real owner notices.

This category is accelerating quickly. The same LexisNexis report found account takeover attempts at login climbing 216% year-over-year, alongside a 450% rise in agentic bot traffic; automated tools now sophisticated enough to mimic human cursor movement and typing cadence. Multi-factor authentication, step-up verification for high-risk actions (address changes, card replacement, credit line increases), and real-time anomaly detection on login and servicing events are no longer "nice to have" controls, they're table stakes. It's also a strong argument for building on modern issuing infrastructure with tokenization and AI-driven monitoring baked in from day one, a shift we cover in postmodern card issuance.

5. Transaction Fraud (Card-Not-Present and Point-of-Sale)

Transaction fraud is what most people mean when they say "credit card fraud" in casual conversation, typically a stolen or cloned card number used to make unauthorized purchases. It splits into two flavors:

• Card-present fraud, via skimming devices at ATMs or point-of-sale terminals, largely mitigated (though not eliminated) by EMV chip adoption.

• Card-not-present (CNP) fraud, where stolen card numbers are used for online or phone purchases. This is the fastest-growing slice of the problem. Fraudsters run automated "BIN attack" scripts that test stolen or algorithmically generated card numbers against merchant checkout pages until one clears.

CNP fraud is a real-time decisioning problem: transaction-level risk scoring, velocity checks, 3D Secure step-up authentication, and merchant-category risk rules all need to run in milliseconds without tanking your approval rates for legitimate cardholders. That tradeoff, catching fraud without frustrating good customers, is one of the hardest calibration exercises in the entire program.

Building a Fraud Strategy, Not a Fraud Feature

Here's the pattern across all five categories: none of them are solved by a single tool. First-party is a underwriting and policy problem. ID theft and synthetic fraud are a verification problem. Account takeover is an authentication and monitoring problem. Transaction fraud is a real-time decisioning problem. A vendor that's excellent at one is often mediocre at the others (if they even cover more than one area), which is why most mature programs run a layered stack rather than a single "fraud solution."

Just as important is ownership. Fraud strategy can't live exclusively with your engineering team, your product team, your operations team, your compliance team, or your card processor — it needs a named owner who understands underwriting, product, and operations well enough to see how a change in one (say, raising credit limits to boost engagement) shifts your exposure in another (bust-out risk). If you haven't yet mapped out who on your team is accountable for that, our guide on the roles you need to launch a credit card program is a good place to start.

Where to Go From Here

Fraud prevention isn't a checkbox you clear before launch. It's a discipline you tune for the life of the program, informed by great reporting and loss data your own portfolio generates. The programs that get this right build fraud strategy into their credit policy and product decisions from day one, not as a patch after the first bad quarter.

If you're building a credit card program and want a second set of eyes on your fraud strategy, or you're not sure which of these five categories poses the biggest risk to your specific product, get in touch with our team. And if you're still shaping the policies and strategies that this all needs to live in, start with how to write a credit policy that actually works.