Stop Bonus Abuse From
Eating Your Growth Budget
Detect fake signups, repeated devices, and suspicious claims before
rewards are lost.
Stats on Bonus Abuse and Market Pain
43%
Promo Abuse Rising
53% of merchants experienced increased promo abuse activity in the past year
0%
Few Merchants Unaffected
Only 7% of merchants said they faced no refund or policy abuse in the past year
27%
Bad Bot Traffic
Bad bots now make up 37% of all internet traffic, increasing automated signup and promo abuse risk
Why Bonus Abuse Prevention Needs More Than Static Rules
Bonus abuse turns growth into hidden loss
A campaign can show strong signups, referrals, and coupon claims while bonus abuse, signup bonus abuse, and promo abuse prevention gaps quietly drain budget and distort acquisition data.
Fake users can look like successful acquisitions
Fraudsters may create accounts with different emails, phone numbers, IPs, and referral codes, but fake account bonus abuse and multi accounting fraud often become clear when device, behavior, and timing signals are connected.
Referral rewards need relationship visibility
A single referral can look normal, but repeated referrals across connected users may reveal referral farming detection, connected account detection, and account farming detection patterns that ordinary campaign dashboards miss.
Bots can exploit incentives faster than teams can review
Scripted signups, coupon testing, fake activations, and repeated reward claims make bot driven bonus abuse, automated signup fraud, and bot signup detection important before bonuses are approved.
Device and behavior signals make review sharper
Static checks can miss users who rotate IPs or identities, while device fingerprinting for bonus abuse, behavioral analysis for bonus abuse, and suspicious device signals help teams prioritize the riskiest claims first.
Risk scoring protects good users from unnecessary friction
The goal is not to block every unusual account. Bonus abuse risk scoring, configurable policies, and review prioritization help teams act on high risk accounts while trusted users continue smoothly.
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Latest from Cross Classify
Solution
Issues We Resolve
We protect your app from the most prevalent cyber attacks
Block Hijacked Accounts
From Claiming Bonuses
Reduces reward abuse from compromised accounts
14%
of consumers said they experienced account takeover in the past year
56%
increase in ATO attacks was recorded in travel and ticketing, where accounts often hold stored value and rewards
How We Prevent Account Takeover
Prevent fraudsters from using stolen or hijacked accounts to claim bonuses, redeem loyalty points, change payout details, or abuse stored promotional value. CrossClassify monitors login behavior, device changes, session anomalies, risky locations, and abnormal account activity to detect account takeover before rewards are moved.
Learn More ❯Continuous Monitoring
Teams often discover bonus abuse after free credits, referral rewards, or coupons have already been used. CrossClassify monitors signup, login, reward, payout, wallet, and redemption activity so suspicious behavior is not treated as a one time event. Bonus Abuse Detection and Prevention helps fraud teams see risk before and after the first signup
Behavior Analysis
Bonus hunters and bots often behave differently from genuine users, even when their profile data looks normal. CrossClassify uses behavioral analysis for bonus abuse to inspect form speed, journey patterns, interaction signals, and abnormal session behavior. This helps teams detect scripted signups and suspicious reward claims without relying only on static fields.
Geo Analysis
Fraudsters can use VPNs, proxies, hosting networks, and region hopping to make repeated claims look unrelated. CrossClassify connects VPN bonus abuse detection, proxy signals, geo patterns, and suspicious device context to help explain why a session looks risky. This gives fraud teams stronger evidence before they review or escalate an account.
Link Analysis
Manual teams often miss referral farms because every account has slightly different data. CrossClassify uses link analysis fraud detection to connect accounts through shared devices, behaviors, referral codes, IP patterns, and session relationships. Bonus Abuse Detection and Prevention helps teams uncover account farms instead of reviewing each bonus claim separately.
Enhanced Security and Accuracy
Support and fraud teams need clear reasons before they challenge a bonus claim or hold a reward. CrossClassify turns bonus abuse risk scoring, suspicious signup signals, device patterns, and behavior evidence into review priority. This helps teams reduce noisy reviews while keeping trusted users moving.
Seamless Integration
Bonus abuse prevention works best when fraud signals are collected inside the real signup, login, reward, and payout journey. CrossClassify supports SDK based integration so teams can collect device, behavior, bot, and risk data across web and mobile apps. This helps connect promo abuse prevention to existing workflows without rebuilding the entire stack.
Alerting and Notification
Teams cannot manually watch every coupon claim, referral reward, or free credit redemption. CrossClassify provides alerts for suspicious signup detection, repeated promotion attempts, risky devices, and connected account behavior. Bonus Abuse Detection and Prevention helps fraud, growth, support, and finance teams act earlier with clearer context.
Compliance
Bonus Abuse Compliance Standards
GDPR and ePrivacy Cookie Consent
Governs consent for tracking scripts, device signals, cookies, and behavioral fraud monitoring.
Case: CNIL fined Google €150M and Facebook €60M in 2022 for difficult cookie refusal.
CCPA and CPRA Privacy Rights
Governs disclosure, opt out, sharing, and tracking rights for fraud signal collection.
Case: Sephora settled for $1.2M in 2022 over CCPA tracking and opt out claims.
Promotion Budgets Stay Protected
CrossClassify detects fake signups, repeated devices, suspicious behavior, and risky reward claims before bonus abuse turns into campaign loss. Growth and fraud teams can protect signup bonuses, free credits, coupons, and referral rewards while trusted users continue with less friction.
Connected Bonus Rings Become Visible
CrossClassify connects accounts, devices, referral links, geo patterns, and behavioral signals to expose fraud rings that look normal when reviewed one account at a time. Teams can find referral farms, multi account fraud, and repeated bonus abuse before those networks drain more rewards.
Risky Claims Get Prioritized First
CrossClassify turns device intelligence, bot signals, behavior patterns, and linked account evidence into clearer review priority. Fraud teams can stop checking every bonus claim equally and focus first on the users, sessions, and reward claims most likely to create loss.
Bots Are Detected Before Rewards Move
CrossClassify detects automated signups, scripted coupon testing, fake activations, and high velocity reward claims before promotional value is redeemed or withdrawn. Bot detection, device fingerprinting, and behavioral analysis help separate human users from high risk automation.
Good Users Keep Moving Smoothly
CrossClassify helps teams reduce bonus abuse without turning every promotion into a heavy verification journey. Risk based decisions allow trusted users to continue smoothly while suspicious accounts, devices, behaviors, and referral patterns receive stronger review.
Customer Stories
Trusted By Security Teams
We wanted better post-login fraud detection, but we didn't want to create another queue of alerts for our team. After adding CrossClassify, manual investigation time dropped by 58%, while we identified 4x suspicious sessions in the first six weeks that our existing controls hadn't surfaced.
58%
drop in manual investigation time
4x
more suspicious sessions identified
Nick Chang
COO, Helfie
Our concern wasn't whether we needed more visibility, we knew we did. It was whether getting it meant rebuilding the WAF and MFA setup we already had. CrossClassify was running alongside our existing stack in under 48 hours, and within the first month it surfaced 37 suspicious sessions we wanted our team to investigate.
<48h
to run alongside existing stack
37
suspicious sessions surfaced in month one
Dr Merran Cooper
CEO, Touchstone Life Care
We weren't debating whether we needed better visibility on our developer portal logins, that part was obvious. What held us back was assuming it meant standing up a whole new alerting pipeline for the team to babysit. CrossClassify slotted in without adding a single new queue, and it cut our team's manual login review time by 46% within the first three weeks.
46%
drop in manual login review time
3 weeks
to achieve the reduction
Anastas Manojlovski
CEO, Roast my IVR
We knew our enterprise SSO logins needed tighter monitoring, no argument there. The hesitation was purely about whether it meant rearchitecting how access works across Teams, WhatsApp, and email sharing. CrossClassify ran alongside our existing setup with zero changes to that flow, and over the following two months it surfaced 3x more suspicious sessions than our previous setup ever caught.
3x
more suspicious sessions surfaced
2 months
to measure the increase
Ali Najmi
CEO, SharePad
Frequently asked questions
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