Promo abuse is when one person claims the same promotional offer repeatedly under multiple identities to extract more value than the offer intends. ShieldLabs links those identities back to one persistent visitor and user across sessions, regardless of email, IP, or browser, so repeat claims surface as a pattern indicating abuse.

Is it illegal to abuse promo codes?

Promo code abuse usually breaches a merchant's terms of service rather than criminal law, so enforcement is a policy decision the merchant makes. Criminal prosecution is rare unless stolen identities or stolen payment methods are involved. What merchants can do: void orders, reverse discounts, close accounts, and refund manipulated transactions under their own terms.

What is the difference between promo abuse and coupon fraud?

Coupon fraud usually means counterfeit physical coupons created or sold for resale, while promo abuse means one shopper creating multiple accounts to claim digital offers meant for new customers. ShieldLabs links each new account to one persistent visitor, so promo code reuse, signup farming, and discount stacking across fake identities surface as a pattern indicating abuse.

What is discount abuse?

Discount abuse is the broad term for any exploitation of merchant discounts by one person operating across multiple identities. It covers first-order code reuse, coupon stacking, voucher fraud, and self-referrals through fake accounts. Merchants sometimes call this discount fraud or voucher abuse, but the mechanism is the same. ShieldLabs links those identities back to one persistent visitor and user, regardless of email, IP, or browser.

What is coupon abuse?

Coupon abuse is when one person redeems a coupon more times than the offer allows, usually by creating multiple accounts. ShieldLabs links each redemption back to one persistent visitor and user, so a coupon reused across many identities surfaces as a pattern indicating abuse, regardless of email, IP, or browser.

What other abuse types relate to promo abuse?

Promo abuse overlaps with several adjacent abuse types that share the same multi-accounting tactic. First-order fraud: an account opened purely to claim the first-order discount, then abandoned.; Refund abuse and policy abuse: where the same multi-accounting feeds both stacked discounts and abused return windows.; Incentive fraud: any signup incentive farmed across multiple identities.

How does ShieldLabs detect promo abuse at signup?

ShieldLabs detects promo abuse at signup by combining several signals into one decision, upstream of the discount being applied. Device identification across browsers, sessions, and cleared cookies.; IP reputation and anonymizer signals: VPN, proxy, Tor, residential ISP versus egress IP mismatch, and anti-detect browser detection.; Pre-built patterns indicating abuse, like many accounts on one device. ShieldLabs runs all of this from a single JS snippet, returning persistent visitor and user identification with a 0–100 Risk Score on every visit.

How is ShieldLabs different from enterprise fraud platforms?

ShieldLabs gives you the full signal set self-serve from $99/month, where enterprise fraud platforms gate the same capability behind five-figure annual contracts and sales-led onboarding. ShieldLabs is ready for promo abuse prevention in 5 minutes from a single JavaScript snippet, no sales call required.

Will ShieldLabs automatically block promo redemptions?

ShieldLabs blocks nothing automatically, it returns a Risk Score and full Details breakdown, and your code decides what to do. Mid-range score: require email or phone verification before applying the discount.; High score: hold the order for manual review.; Low score: let the legitimate shopper through with no extra friction.

Promo Abuse Prevention

Prevent promo abuse from draining marketing budgets

Keep first-order discounts, promo codes, and referral credits on real customers, and acquisition spend on real growth

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Stop fake signup waves

Stop disposable-email farms draining promo budgets at scale

Block repeat promo redemptions

Spot the same person across emails, IPs, and devices claiming the same code twice

Protect acquisition spend

Keep first-order discounts, referral credits, and welcome offers driving real growth

Keep real shoppers friction-free

Legitimate buyers complete checkout without extra verification or holds

How ShieldLabs prevents promo abuse

ShieldLabs combines 100+ device, OS, browser, IP and network layer signals, surfacing anonymity signals and abuse patterns so your team can stop fraudulent promo redemptions in real time

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, incognito mode, and rotated IP

Anonymous Traffic Detection

VPN, proxy, Tor, anti-detect browser, IP reputation and other anonymity signals

Ready Risk Score

A ready-to-use risk model that scores every session 0–100 with signal breakdown

Detect abuse with in-depth pattern analytics

Pre-built patterns indicating multi-accounting, account farms, and other abuse patterns

Real-Time Analytics

Aggregate traffic risk score, per-session signal breakdown, and active abuse patterns

Start preventing promo abuse in 5 minutes

Easily integrate into any signup, checkout, or promo redemption flow

1
Sign up
Sign up and get 5,000 free identifications
2
Install snippet
Install one JavaScript snippet on your signup or checkout page
3
View analytics
View detailed analytics on anonymity signals, risk scores, and potential abuse in your traffic
4
Use API & Webhooks
Use the API and Webhooks to prevent abuse

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "ip": "62.197.149.124",
  "country": "Lithuania",
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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