How to Prevent Promo Abuse with OSINT

Promotional campaigns and signup incentives are essential tools for digital platforms looking to attract and retain users. Whether it’s a welcome bonus in a gaming app, a referral reward in a fintech product, or a first-time discount in an e-commerce storefront, these offers serve a purpose: to drive growth. But as the volume of incentives increases, so too does a more insidious trend—promo abuse and bonus abuse fraud.

These aren’t isolated incidents of opportunism. In many cases, they’re systematic operations involving scripted signups, device farming, and the use of disposable contact details to exploit promotions at scale. Beyond the immediate financial losses, repeated abuse of promotions erodes user trust, distorts customer data, and burdens onboarding systems with noise that’s difficult to clean up. 

To detect this abuse early—ideally before the reward is ever triggered—companies are turning to OSINT (Open Source Intelligence) and digital footprint analysis. This article explores how OSINT supports that mission and why traditional controls are no longer enough.

What is promo abuse, and why is it growing?

Promo abuse refers to exploiting discounts, welcome offers, and bonus schemes by users or groups of users, acting in bad faith. These actors may create multiple accounts to repeatedly access incentives, use automation to generate referrals, or disguise their identities through proxies and fake contact details.
How legitimate it can appear at surface level makes promo abuse fraud particularly difficult to combat. A new signup with a unique email and working phone number might pass basic checks, even though both were generated minutes ago for the sole purpose of accessing a promo code. When this is done at scale, the financial cost adds up quickly. But the damage isn’t only monetary: customer data becomes unreliable, user acquisition metrics are skewed, and manual cleanup operations strain internal teams.

This issue is most commonly associated with the iGaming sector, where welcome bonuses are directly tied to initial deposits or engagement. However, the threat has steadily expanded into other industries:

• E-commerce platforms offering first-time buyer discounts are frequently targeted by users creating duplicate accounts to re-trigger incentives.
• Fintech services, particularly digital banks and wallets, are vulnerable through referral programs and new business onboarding offers. Sophisticated abuse operations even register shell companies to repeatedly access these incentives, creating downstream KYC and compliance challenges.
• Subscription apps and loyalty-based platforms may also face repeat signups using slight variations in identity or device setup.

As businesses continue to rely on incentives to drive growth, promo abuse has evolved into a scalable, infrastructure-backed fraud model. Detecting it requires more than recognizing duplicates—it demands the ability to interpret digital identity signals in context, which is where OSINT becomes especially effective.

Why promo abuse fraud requires signal-based detection

A working phone number. A valid email address. A sign-up from an allowed region. These details used to be enough to consider a user legitimate. But in the context of promo abuse fraud, these surface-level checks often provide a false sense of security.

Incentive abusers have access to increasingly sophisticated tools: virtual SIM services that issue thousands of disposable phone numbers, browser emulation tools that mimic real user behavior, and fresh email addresses spun up in seconds. These setups are designed to pass traditional validation tests, especially those that only verify whether a piece of information exists, not whether it can be trusted.

The key difference lies in the depth of information. Basic checks answer the question: “Does this user have the required fields?” Signal-based detection goes further: “Does this user’s digital footprint make sense?”

Some examples of what shallow verification misses:

• An email address that’s valid, but brand new, and never seen across trusted activity sources
• A phone number that works, but belongs to a provider known for disposable VoIP services
• An IP address from a legitimate country, but one linked to a known proxy network or VPN exit node

These signs don’t always stand out in isolation. But signal-based detection works by inspecting the relationships between them, flagging inconsistencies in how a user appears versus how a trustworthy user would behave or register.

For businesses facing scalable promo abuse attacks, this level of visibility is essential. Relying solely on field validity doesn’t prevent synthetic identity setups from passing through onboarding. The goal is to understand whether the identity presented is coherent, not just technically complete.

How OSINT strengthens promo abuse detection

OSINT (Open Source Intelligence) refers to data gathered from publicly available and commercially accessible sources. In fraud prevention, it’s not about social media deep dives or investigative journalism. It’s about extracting value from the signals users leave behind: their contact details, device environment, network origin, and behavioral patterns.

When applied to promo abuse, OSINT helps uncover setups that are technically functional, but contextually suspicious. It transforms passive fields like an email address or phone number into high-context indicators of trust or risk.

Some of the most effective OSINT data points for detecting promo abuse fraud include:

Email Address Intelligence
Not all email addresses are created equal. Key data signals include:

• The age of the address or domain
• Whether it’s linked to previous breaches
• If it belongs to disposable or low-reputation providers
• Reuse across multiple signups in short intervals

Phone Number Metadata
A phone number might pass SMS validation, but its background tells a different story:

• VoIP vs. mobile carrier classification
• Number portability and usage patterns
• Country-specific anomalies or volume clusters
• Known disposable number pools

IP and Network Behavior

The IP address alone offers limited insight—context reveals more:

• Proxy or VPN routing
• Hosting provider vs. residential ISP
• Traffic anomalies from specific geolocations
• IP ranges reused across many signups

Device and Browser Setup
Fraud operations often run on emulated environments or hardened browsers:

• Missing entropy in device traits
• Shared fingerprints across multiple users
• Mismatch between browser language, OS, and IP location

Each of these elements, when viewed through the OSINT lens, becomes part of a broader risk picture. Individually, they might seem harmless. But patterns emerge when this data is correlated—and those patterns often point to orchestrated abuse. What OSINT offers is context at scale. For promo fraud teams, it means being able to detect not only what a user submits, but whether the submission fits the profile of a real, trustworthy customer.

How digital footprint analysis exposes promo abuse fraud

Bonus abuse fraud often hides behind data points that, on their own, don’t raise suspicion. A clean IP, a working phone number, and an active email address can all pass verification, especially when they’ve been deliberately selected or fabricated to do exactly that.

This is where digital footprint analysis becomes essential. It looks beyond individual inputs and examines how they fit together—how consistent, plausible, and traceable they are when viewed as part of a single user session. Some of the most telling combinations include:

• Newly registered emails tied to numbers from disposable VoIP providers
  On their own, these inputs might pass as legitimate. But when paired, they suggest synthetic identity setups intended for short-term use.
• Repeated logins from the same device using slightly different emails and phone numbers
  This often points to multi-accounting, especially when the browser or device fingerprint remains stable while identity details change.
• IP locations that shift between sign-ups but share device or behavioral signatures
  Attackers may rotate IPs to appear distributed, but reused environments or identical header data indicate automation.
• Domain names created days before sign-up in business referral schemes
  This is particularly relevant for fintech products offering bonuses to new business customers. A company website might exist, but its digital trace reveals it was created solely to pass validation.

Each of these scenarios can be caught early—before the promo or bonus is granted. That timing matters. Waiting until a reward is redeemed to investigate fraud is not only inefficient, it often means the damage is already done. Footprint analysis allows teams to move fraud detection upstream. Instead of chasing red flags after abuse has occurred, companies can assess risk at the point of entry, quietly and at scale.

Building resilient promo abuse detection strategies with OSINT

Promo abuse isn’t a fixed threat—it shifts constantly. What works today for fraudsters may not work tomorrow, and the tools used to exploit incentives are always evolving. New IP ranges, freshly registered domains, reissued phone numbers, and browser automation kits all make it easy to replicate human behavior on the surface. That’s why detection strategies need more than rigid rules or blocklists—they need flexibility backed by live intelligence.

OSINT data plays a critical role in helping systems stay relevant without constant manual updates. When pulled from the right sources and kept up to date, OSINT-based detection can:

• React to new disposable phone number providers entering the market
• Spot clusters of suspicious domains as they emerge
• Detect shifts in traffic patterns linked to organized multi-account abuse
• Flag behavior that wasn’t previously suspicious, but now forms part of a wider pattern

Rather than relying on predefined fraud scenarios, OSINT empowers teams to detect new combinations of signals that suggest fraud, even if those combinations haven’t been seen before.

This flexibility is essential for scaling defense without blocking genuine users. Businesses offering time-sensitive promotions or automated onboarding flows can’t afford lengthy review cycles. They need systems that absorb fresh intelligence, interpret risk in real time, and adapt quickly to the changing ways fraudsters operate.

Resilience in this context isn’t about locking the doors harder—it’s about knowing who’s approaching, what tools they’re using, and how their identity aligns (or doesn’t) with what trustworthy behavior looks like.

Silent onboarding protection with Trustfull

Detecting bonus abuse doesn’t have to disrupt the user experience. Trustfull helps platforms evaluate risk before a reward is ever issued—silently and without friction. Instead of relying on interaction-based challenges, it’s essential to inspect the digital context behind each session: device setup, network environment, and the history of contact details provided.

Key signals include:

• Graph-based intelligence to detect account clustering and shared infrastructure
• Detection of VPNs, Tor nodes, and proxy usage to unmask location spoofing
• Disposable phone number tracking, powered by a growing global dataset
• Newly registered email monitoring to flag accounts likely created for abuse

So, how does promo abuse prevention work in practice, and how can it be prevented? You can find out how the EU iGaming company Snaitech utilized email and phone number alternative data to pinpoint legitimate player accounts effectively, silently, without impacting the user experience.
Combined, these capabilities form a transparent scoring model that adapts to business-specific logic. Fraud teams can explore flagged sessions in detail, customize risk thresholds, and stay ahead of new abuse patterns without overburdening legitimate users.

Blocking it requires more than binary filters. It takes depth. OSINT-powered detection gives fraud teams the context they need to make fast, confident decisions at the point of entry.

If you want to find to learn more about OSINT’s role in this type of scam, talk to our fraud prevention experts, and discover when signals align to suggest abuse; everything from disposable numbers, rushed domains, and synthetic identities—learn when it’s possible to act.

Questions and answers

What makes detecting promo abuse harder than other types of fraud?
Promo abuse often involves real users and technically valid data—working emails, phone numbers, and plausible locations. Unlike payment fraud, there’s rarely a clear financial anomaly or chargeback to trigger investigation. The abuse is buried in volume and repetition, making it harder to detect without connecting patterns across sessions, devices, and identities. This is why signal correlation and identity context are more effective than single-point checks.

What’s the difference between promo abuse and referral fraud?
Promo abuse typically targets incentives such as welcome bonuses, first-time discounts, or cashback offers by creating fake or duplicate accounts. Referral fraud, on the other hand, involves manipulating referral systems—often by creating two linked accounts, one posing as the referrer and the other as the referee. While both rely on identity spoofing and account repetition, referral fraud usually aims to trigger two-sided rewards, adding complexity to detection efforts. Both can occur together as part of broader incentive abuse schemes.

Can OSINT help detect business entity abuse in B2B incentive programs?
Yes—OSINT can reveal red flags in newly registered business domains or suspicious digital footprints tied to corporate identities. Fraudsters may create shell companies or manipulate business onboarding processes to access high-value B2B promotions, especially in fintech or payments. OSINT sources can provide intelligence on domain registration dates, linked social presence, and company credibility indicators. This helps distinguish between legitimate new customers and entities set up purely to exploit incentive schemes.