Stop loyalty fraud. Protect points and rewards programs

Loyalty fraud is the practice of exploiting or extracting value from a loyalty rewards program through fake accounts, stolen logins, or manipulated redemptions. Operators usually break it into layers, each handled by a different tool:

• Signup layer (fake-account farms): one person or a coordinated farm opens many accounts to harvest welcome bonuses, referral credits, and stacked sign-up rewards, including synthetic identities that pool points across memberships for a richer redemption.
• Login layer (account takeover): the primary entry point for rewards fraud, where fraudsters use leaked passwords from data breaches and credential stuffing to access real members' accounts. They can drain balances before the victim notices, since members check loyalty balances far less often than bank accounts.
• Redemption layer (points fraud): the value-extraction moment where mileage fraud, point conversion, and gift-card cash-out turn dormant points into liquid currency.
• Employee layer (internal fraud): staff comping points to friends or inflating balances with fake transactions.

Each layer maps to its own tool: a KYC platform for identity verification on high-value redemptions, a bot-management tool for credential stuffing at network scale, a payment-fraud platform for gift-card and chargeback fraud, and an internal fraud-monitoring system for employee abuse.

ShieldLabs covers the web-side identity layer at signup, login, and redemption. It detects multi-accounting behind fresh signup farms and account takeover from unrecognized devices, using persistent visitor and user identification that holds across sessions regardless of email, IP, or cleared cookies.

Rewards fraud is the extraction of value from a rewards program through stolen credentials, fake accounts, or pooled synthetic identities. It is another name for loyalty fraud, with three common vectors:

• Account takeover: stolen credentials liquidate rewards balances into gift cards, statement credits, and award redemptions.
• Fake-account farms: fresh signups harvest welcome bonuses at scale.
• Synthetic identity pooling: rewards accumulate across coordinated memberships for a single richer redemption.

Mastercard industry data puts annual rewards-fraud losses at $1B to 3B across travel, hospitality, retail, and credit-card programs.

ShieldLabs addresses the rewards fraud vector at signup, login, and redemption by detecting multi-accounting and account takeover as High-Risk Events. It is the identity layer that sits in front of KYC, payment-fraud, and bot-management tools.

Points fraud is the unauthorized conversion of accumulated loyalty points into liquid value such as gift cards, miles, award flights, or third-party marketplace cash. It usually surfaces at the redemption moment, once a member's account has been taken over or points have been harvested across fake accounts and the balance is liquidated before the real member notices. The dominant vectors are:

• Credential-stuffing account takeover that reaches an existing member's balance.
• Mileage fraud: retroactive flight-credit claims and award-ticket booking with stolen miles.
• Synthetic-identity point pooling across coordinated memberships.

ShieldLabs addresses points fraud at the web-side identity layer by detecting account takeover from unrecognized devices and flagging anonymized connections and redemption-time anomalies.

Rebate fraud is claiming rebates the claimant is not entitled to, typically through fake receipts, duplicate accounts that claim the same rebate repeatedly, or refund-and-rebuy cycles that game per-purchase caps. In retail and grocery loyalty programs the dominant vectors are:

• Fake-account farms creating fresh signups to harvest first-purchase rebates at scale.
• Synthetic identities claiming rebates across coordinated memberships.
• Refund-and-claim cycles where one purchase triggers multiple rebate payouts.

It shares its claim mechanics with promo abuse and cashback fraud, but runs on the same identity-layer mechanism underneath.

ShieldLabs addresses rebate fraud at signup, login, and claim time by detecting multi-accounting and identity reuse across membership IDs.

Several adjacent abuse types share loyalty fraud's account-identity layer, even though their payouts differ. The common ones are:

• Affiliate and referral fraud: coordinated accounts farm referrals to harvest commission in programs that pay for new sign-ups.
• Coalition fraud: one fake identity pools points across partner brands in cross-merchant programs, then redeems for a single richer reward.
• Cashback fraud: fake transactions or coordinated refunds trigger cashback payouts in card and ecommerce loyalty programs.
• Tier-status fraud: refund-and-rebuy cycles game program rules to qualify for elite-tier benefits.
• Membership fraud: synthetic memberships harvest sign-up bonuses, beta perks, or referral payouts.

ShieldLabs detects the same person operating many accounts (multi-accounting) and stolen credentials reaching real ones (account takeover). That identity-layer signal is shared across all of these abuse types.

Preventing loyalty fraud takes several layers, because no single tool covers all of it. Operators usually combine:

• KYC layer: an identity- or document-verification platform that checks IDs on high-value redemptions and large point cash-outs.
• Network layer: a bot-management tool that blocks credential-stuffing waves and automated signup floods.
• Payment layer: a chargeback or payment-fraud platform that screens card transactions on gift-card purchases and reward redemptions.
• Policy layer: your own program rules, such as point-expiration windows, redemption velocity limits, mileage-transfer geo restrictions, and manual-review thresholds on high-value cash-outs.
• Account layer: an identification tool that surfaces one person operating many accounts and logins from unrecognized devices, feeding fake-account-farm, account-takeover, and points-farming analytics so you can hold or step up at the right moment.

Most loyalty programs run these layers in parallel, since fraud usually slips through wherever no tool is watching.

ShieldLabs covers the account layer. It returns persistent identification, a risk score, and High-Risk Events at signup, login, and redemption, so you can act before a synthetic member finishes onboarding or a stolen balance ships.

ShieldLabs runs from one JavaScript snippet on your signup, login, or redemption page and detects loyalty fraud in real time. On every visit it returns:

• A 0-100 risk score with a per-signal breakdown.
• Persistent visitor and user identification that holds across cleared cookies, incognito, and rotated IPs.
• Risk signals: VPN, proxy, Tor, anti-detect browser, IP reputation, and others.
• High-Risk Events: multi-accounting, account sharing, impossible travel, and account takeover.

High-Risk Events are detected automatically. When many signups trace back to one device, browser, or identity, ShieldLabs detects multi-accounting; when a login or redemption comes from an unrecognized device or a country far from the member's usual one, it detects account takeover or impossible travel.

ShieldLabs delivers the score, signals, and High-Risk Events through the analytics dashboard, API, and Webhooks, so you can flag, hold, or step up before a fake member harvests a bonus or a stolen redemption ships.

Yes. The mechanism is the same wherever one person can profit by opening many accounts or getting into an account that is not theirs, so ShieldLabs applies across every loyalty-program category. It detects multi-accounting and account-takeover logins at signup, login, and redemption, and stops the downstream abuse each category cares about:

• Airline frequent-flyer: mileage fraud, retroactive flight-credit claims, award-ticket booking with stolen miles, and mileage transfer to drop accounts.
• Hotel rewards: free-night redemption with stolen points, sign-up-night farming, and account takeover for elite-status theft.
• Retail and grocery: first-purchase-bonus farms, rebate refund-and-claim cycles, and points farming through coordinated low-value transactions.
• Credit-card rewards: points converted to gift cards or statement credits, synthetic-identity card-loyalty signups, and cash-out to third-party marketplaces.
• Restaurant and franchise: employee point-comping, fake member signups, and refund-and-rebuy tier gaming.
• Coalition and cross-merchant: points pooling across synthetic identities and cross-program arbitrage.
• Web3 quest and reward: Sybil-account farms harvesting quest points and airdrop allocations across coordinated wallets, plus takeover of connected wallets.

In every case ShieldLabs identifies the visitor and user behind the account at the web-side signup, login, and redemption layer, and you choose whether to allow, hold, or step up the request.

Yes. ShieldLabs is loyalty fraud prevention software focused on the web-side identity layer at signup, login, and redemption. Loyalty fraud prevention software is a tool an operator integrates to run loyalty fraud detection automatically instead of reviewing accounts and redemptions by hand. ShieldLabs provides that through:

• One JavaScript snippet on your member-facing pages.
• A risk score and risk signals returned via API and Webhooks.
• High-Risk Events in the analytics dashboard, API and webhooks.

It identifies the visitor and user, detects High-Risk Events, and flags risky accounts and redemptions, so you can allow, hold, or step up each one.

ShieldLabs is the software that covers the fake-account, multi-accounting, account-farm, and account-takeover layer underneath most identity-side loyalty fraud.

Integration takes about 5 minutes: add one JavaScript snippet to your member-facing pages and read the returned identification and risk data from the API. The steps are:

• Install the snippet on your signup, login, and redemption pages, including the member portal, rewards-redemption flow, and points-transfer flow.
• Pass an optional UserHID (a hashed member identifier from your own system) once a member signs up or logs in.
• Read the results on the next visit: the risk score, risk signals and High-Risk Events come back via API and Webhooks for server-side flows, and in the analytics dashboard.

ShieldLabs returns the first risk score on the member's first visit, and the free tier includes 5,000 one-time identifications, so you can see the results on your own traffic first.

ShieldLabs flags risky members and helps block fraudulent and abusive traffic. It returns a risk score, a full signal breakdown and High-Risk Events, and you choose the action for each score band. Typical setups look like:

• Suspicious scores (30-59): flag a signup for review, or require email or phone verification before sign-up-bonus eligibility.
• Suspicious scores on redemptions: trigger step-up authentication above a points threshold.
• Dangerous scores (60-100): hold a high-value redemption or an unrecognized-device login for manual review.

The free tier includes 5,000 identifications, so you can see the results on your own traffic first.