Published on 2026-02-18
Detect and prevent abuse of referral programs, promotions, and reward systems.
Detect and prevent abuse of referral programs, promotions, and reward systems.
Incentive abuse can cost your business significantly. Our detection identifies fraudulent reward claims, referral abuse, and promotion manipulation to protect your bottom line.
Marketing incentives like referral bonuses, welcome offers, and promotional discounts are powerful tools for customer acquisition and retention. However, they're also prime targets for abuse. Fraudulent users exploit these programs by creating fake accounts, manipulating referral systems, or claiming promotions multiple times. The cost can be substantial—not just in terms of the rewards themselves, but also in skewed marketing metrics, wasted acquisition spend, and damage to program effectiveness. Our comprehensive detection system identifies these abuses while preserving the positive experience for legitimate users.
Identify fake referrals and self-referrals by analyzing account relationships, device sharing, and behavioral patterns.
Self-referral fraud occurs when users create multiple accounts and refer themselves to claim referral bonuses. We detect this by analyzing device fingerprints, IP addresses, payment methods, and behavioral patterns to identify when the referrer and referee accounts are controlled by the same person or group.
Detect when users create multiple accounts to claim the same promotion multiple times, preventing revenue loss.
Limited-time promotions and welcome offers are frequently abused by users creating multiple accounts. Our system tracks account creation patterns, device usage, and promotion claim history to identify when the same person is claiming promotions multiple times through different accounts.
Validate reward claims by checking account authenticity, usage patterns, and eligibility criteria before processing rewards.
Before processing any reward claim, we verify that the account meets all eligibility requirements. This includes checking that the account is legitimate (not synthetic), has met minimum usage requirements, and hasn't already claimed similar rewards. We also verify that the account shows genuine engagement rather than being created solely to claim rewards.
Automatically block suspicious reward claims and flag accounts for review, protecting your incentive programs in real-time.
When suspicious activity is detected, our system can automatically block reward claims, flag accounts for manual review, or require additional verification. This happens in real-time, preventing fraudulent rewards from being processed and protecting your marketing budget.
Users create multiple accounts and refer themselves to claim referral bonuses. This is one of the most common forms of referral abuse and can quickly drain your referral program budget.
Detection: We analyze account relationships, device sharing, payment method overlap, and behavioral similarities to identify self-referrals. Accounts that refer each other in circular patterns or show identical usage patterns are flagged.
Users create multiple accounts to claim the same promotion or welcome bonus multiple times, often using different payment methods or identities to avoid detection.
Detection: We track promotion claims across accounts, identifying when multiple accounts claim the same promotion from the same device, IP address, or payment method. We also detect rapid account creation patterns that coincide with promotion launches.
Coordinated groups create multiple accounts and refer each other in networks to maximize referral rewards, often using automation tools to scale the abuse.
Detection: We identify referral networks by analyzing referral graphs and detecting patterns where accounts refer each other in organized structures. We also detect automation through timing patterns and behavioral analysis.
Users create accounts solely to claim rewards without genuine engagement, then abandon the accounts or use them minimally to maintain eligibility.
Detection: We analyze account engagement levels, usage patterns, and activity quality to identify accounts created primarily for reward farming. Accounts with minimal legitimate usage but maximum reward claims are flagged.
Every fraudulent reward claim represents direct financial loss. For programs with thousands of participants, even a small percentage of abuse can result in significant costs. This includes not just the reward value itself, but also processing fees and administrative overhead.
Fraudulent accounts inflate your customer acquisition metrics, making it difficult to measure the true effectiveness of your marketing programs. This can lead to poor decision-making and wasted marketing spend on ineffective channels.
When abuse becomes widespread, legitimate users may lose trust in your programs, reducing participation and effectiveness. You may also need to reduce reward values or tighten eligibility requirements, which can hurt legitimate users.
Fake accounts created for abuse consume platform resources, support bandwidth, and infrastructure capacity without providing value. They also create noise in your analytics, making it harder to understand your real user base.
We use cookies to improve your experience and anonymously analyze usage.
