Promo abuse is one of the most prominent forms of consumer fraud in the gig economy. It can be subtle enough to go undetected, scalable enough to drain millions from promo budgets, and sophisticated enough to bypass most traditional defenses.
In fact, promo abuse accounted for nearly half (48%) of all consumer fraud that Incognia detected on gig economy platforms in 2024.
In a recent webinar, three fraud leaders came together to unpack this silent threat and share what’s working to stop it:
They covered the tools that enable scaled abuse, the signals that help distinguish real users from fraud rings, and the cross-functional strategies that help you detect abuse before it spreads.
At first glance, promo abuse may not look like a problem. In fact, it often looks like success.
A campaign gets a flood of redemptions, new signups skyrocket, and orders start flying out the door.
But when fraud prevention teams dig deeper, the picture shifts.
Danielle explained it perfectly:
Promo abuse is a very silent kind of fraud. It doesn't represent an obvious loss that will cause a direct or even immediate financial impact like we see with chargebacks or account takeover, for example. So it becomes really difficult to detect and clarify that.
What seems like high campaign performance might actually be driven by fraud. Under a fraud lens, you might find that 80% of coupon redemptions come from just four or five devices. Or that 50% to 70% of newly created accounts share a common data point—like a phone number, IP address, or email format. These are signs of coordinated abuse
The real question becomes: Are these campaigns attracting legitimate users—or fueling scaled fraud?
Sudhir reinforced that data alone isn’t enough:
Just looking at top-line metrics isn’t going to help you. We call this manufactured growth versus real growth. You need to be able to identify who your real users are—and who’s just abusing the platform.
He emphasized the importance of combining behavioral analysis with intent-based signals. While some users may bend the rules a few times to redeem extra discounts, others are orchestrating large-scale abuse operations.
Fraud teams need to distinguish between the two—and they can’t do it by relying on surface-level KPIs.
Promo abusers aren’t just a handful of users creating second accounts for extra discounts. They’re often organized operations using sophisticated tools to scale fraud like a business.
What once might have involved a few reused email addresses has evolved into full-scale fraud rings equipped with automation, spoofing, and app tampering tools. Many are coordinating across channels like Telegram and Discord, sharing tactics that evade most basic fraud defenses.
Fraudsters use a growing arsenal of tools, including:
And they’re not doing it alone. Many of these capabilities are available through Fraud-as-a-Service (FaaS) vendors that package tampering tools into slick, tiered products that fraudsters can simply buy off the shelf.
Suhdir describes some tactics he’s seen at Grubhub:
There are huge device farms out there. We’ve seen fraudsters creating multiple new accounts at the same time—either using emulators or even large-scale human farms. We’ve also seen synthetic identities being used, like a real social security number paired with a fake name or phone number to create multiple fake personas. This is the scale of what we’re up against.
Gen AI introduces even more complexity on both sides. Fraudsters are using bots to mimic legitimate users and bypass defenses. Meanwhile, real customers are starting to use tools like ChatGPT to interact with apps in unexpected ways.
Danielle shared an example from Zé Delivery:
We had a real customer who used ChatGPT to place an order on our web app…and we actually delivered the order. So we can’t just block this kind of usage outright. At the same time, we have to be able to detect when GenAI is being used by fraudsters.
The rise of Gen AI tools and other advanced tactics is pushing fraud beyond the reach of traditional defenses. Fraudsters can spin up endless simulated devices, slip synthetic identities past basic checks, and use tampering tools to mask or randomize many of the static attributes a system might depend on. These aren’t edge cases anymore—they’re everyday realities.
This is why deeper, layered signals—like location behavior and device integrity—are critical to cut through the noise.
Promo abuse doesn’t always look like traditional fraud. There’s no chargeback, no stolen credit card, no account takeover alert. Instead, fraudsters quietly blend in with legitimate users—sometimes mimicking real behavior to try to fly under the radar.
This makes the line between abuse and growth difficult to define. Some typically good users might simply create a second account to redeem a discount twice. But others are part of a coordinated operation running hundreds of fake accounts behind spoofed signals.
Sudhir explained how Grubhub evaluates this gray zone:
The first thing I look at is intent. If a user is bending the rules a bit—maybe creating a few extra accounts to take advantage of new user or reactivation promos—I think that’s generally fine, unless their long-term value ends up being a loss for the company. But when you move into scale and deception, that’s where it becomes abuse. If someone is deliberately creating fake accounts, using false identities, or coordinating efforts to exploit promos, that’s clearly fraud.
Behavioral patterns are often the clearest signals of abuse. That includes things like:
Danielle added that even subtle indicators can reveal bigger problems if you’re monitoring the right data.
It’s really important to have strong monitoring in place alongside your blocking tools. You need to track data and behavior patterns over time to identify loopholes. For example, you can monitor the velocity of coupon usage within a specific campaign, analyze coupon usage per device, or look for a concentration of orders at the same delivery address or geolocation. These KPIs can signal that a campaign is being abused—and when that happens, you need to act quickly by updating your rules and blocking those new patterns.
Promo abuse often lives in the gray area. Fraud teams need visibility across behavior, devices, and location to catch it early—before abuse gets mistaken for growth.
Traditional fraud signals—like basic device IDs or IP addresses—aren’t enough to catch modern promo abuse. Fraudsters can spoof or reset these values with ease, making it difficult to track repeat behavior across devices or accounts.
Danielle noted how these tactics have evolved. Fraudsters now use app cloners, device emulators, and spoofing tools that can mask device identification entirely. If a platform relies on basic device detection alone, it’s already behind.
That’s why layered signals are critical—especially signals that are harder to manipulate.
Jeniffer explained that many platforms make the mistake of relying on advertising IDs to track users. But for fraudsters, resetting that ID can be as simple as reinstalling the app. Without something more persistent, the fraudster will look like a new user.
She points to stronger, more tamper-resistant signals:
Danielle highlighted how this layered approach plays out in practice:
Even if a fraudster resets their device, if they keep using it from the same place, we can still detect them.
Sudhir added that clustering behavioral signals helps Grubhub detect coordinated abuse that might not be visible in isolation. It’s especially useful for identifying collusion across accounts or repeated attempts that span multiple app sessions or devices.
With these advanced identity signals, fraud prevention teams can spot returning fraudsters even after resets, device swaps, or account changes. That means smarter rules, fewer false positives, and a stronger line of defense against abuse that would otherwise fly under the radar.
Stopping promo abuse at checkout prevents losses, but it’s still a last-minute interception that could skew growth metrics. Some teams may choose to focus their fraud detection efforts earlier in the user journey.
Sudhir shared that Grubhub is moving upstream, aiming to assess risk even before a promotion is delivered. Instead of trying to block fraud at the point of redemption, the goal is to identify risky users and behaviors before the promo is ever sent.
This proactive approach reduces fraud while preserving a better experience for real users. When teams identify fraud earlier, they can apply friction selectively to high-risk users and keep trusted ones moving freely.
That distinction matters. Not all flagged users are bad actors. Some are loyal customers pushing boundaries for a better deal. Others may be worth retaining despite low-level abuse. Catching issues earlier gives teams more options—like limiting promo access or tracking behavior over time instead of defaulting to blocks.
Too often, fraud prevention is seen as a roadblock to growth—an opposing force to marketing and customer acquisition. But when done right, fraud prevention is actually a growth enabler.
Success, of course, depends on context and strategy should evolve with the business. A smaller company focused on brand awareness might accept more abuse to bring in new users. A more mature platform may instead prioritize profitability and retention.
What matters is matching the strategy to the phase the business is in and making intentional decisions about what risks to accept.
Sudhir reinforced that alignment means more than policy—it means communication. Fraud teams need to speak the same language as product, marketing, and finance. That includes reframing traditional KPIs like “fraud rate” in terms of customer lifetime value, budget efficiency, or campaign ROI.
Ultimately, growth and fraud prevention aren’t in conflict. When teams align on what kind of growth they’re aiming for—and what risks they’re willing to accept—they can build smarter defenses that protect revenue and preserve trust.
Stopping promo abuse takes more than just a capable fraud team. It requires coordination across product, marketing, support, and engineering.
Sudhir emphasized that fraud signals shouldn’t be siloed. At Grubhub, his team shares signals across the entire marketplace—diners, drivers, and restaurants—because fraudsters often operate across roles. A bad actor exploiting diner promos might also be behind a fake restaurant or colluding with another user. Connecting the dots across systems makes these patterns easier to catch.
He also stressed the importance of involving engineering early. Long-term fixes like risk-based promo delivery or device identification enhancements often depend on engineering resources. Getting those priorities on the roadmap requires clear alignment.
Support teams also play a role. Sudhir noted that customer service agents are often the first to hear about unusual behavior or abuse. Building a feedback loop between fraud and support helps teams respond faster and stay ahead of emerging tactics.
Danielle echoed the value of cross-functional alignment, especially with marketing. Fraud prevention teams need to provide visibility into campaign risks and share insights in ways that help growth teams make smarter decisions, rather than just shutting down a campaign idea.
And across all functions, the most important tool is shared understanding: of what abuse looks like, what the business is optimizing for, and how each team contributes to smarter, safer growth.
Jeniffer highlighted what happens when teams don’t align early.
In one case, a delivery platform’s fraud team flagged a campaign as high-risk, but the marketing team moved forward anyway. After launch, they estimated that 90% of the promo budget was lost to fraud.
That kind of misalignment isn’t rare. But when marketing, fraud, and engineering teams collaborate early, platforms can design promotions that drive real growth without opening the door to scaled abuse.
Promo abuse is one of the most important threats facing gig economy platforms today. It operates quietly, can spread quickly, and often goes undetected until much damage is done.
The platforms that stay ahead are the ones treating promo abuse as a business-critical issue, rather than a cost of doing business. They invest in powerful signals like location intelligence and device integrity, monitor for subtle behavioral patterns, and align teams across fraud, product, marketing, and engineering.
With the right collaboration and tools in place, platforms can shut down abuse without sacrificing growth and ensure promotions reach the users who actually drive long-term value.
Stopping promo abuse takes layered signals that can spot repeat offenders.
Incognia helps fraud teams close those gaps with a combination of persistent device identification, device integrity checks, and location intelligence. This approach makes it possible to detect when multiple “new” accounts are tied to the same device or environment—even after resets, spoofing, or the use of app cloners.
For promo campaigns, this gives platforms the ability to:
By linking accounts to the devices and environments fraudsters actually use—not just to easily changeable identifiers—Incognia gives platforms the context to block scaled promo abuse while keeping the experience frictionless for legitimate customers.