--- title: Policy Abuse source_page: https://www.sardine.ai/first-party-fraud canonical: https://www.sardine.ai/first-party-fraud format: text/markdown description: --- **Quick links:** [Human page](https://www.sardine.ai/first-party-fraud) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/insights/blog) · [Demo](https://www.sardine.ai/demo) --- # Policy Abuse ## Stop policy abuse without impacting legitimate customers Detect early signs of promo abuse, refund fraud, and chargeback fraud before they turn into losses. ## Strong abuse protection for merchants, marketplaces, and platforms - 5.4B+: Profiled devices - 787M: Consumers Screened ### Stop hidden revenue leaks Use cross-industry consortium insights to uncover fraudsters and patterns that would be invisible in your own data alone. ### Shield your best customers Keep the experience smooth for your users with targeted controls that stop abuse without broad restrictions. ### AI that adapts defenses Agents analyze your traffic and proactively recommend rules to prevent fraud attacks and abuse patterns ## Leverage device, behavior, and consortium signals to stop abuse earlier ## Prevent promo abuse Promo Abuse Reduce promo farming, code sharing, and incentive stacking without adding friction for legitimate customers. ### Detect multi-accounting Surface accounts sharing the same device fingerprints, phones, names, or emails to create accounts. ### Uncover coordinated abuse Identify referral rings and organized farming by correlating activity across accounts, sessions, and the broader network. ### Enforce network-level limits Apply controls at the user, account cluster, or network level so you can stop abuse without broad promo restrictions. ## Protect margins while keeping policies flexible Refund Abuse Prevent users from exploiting your refund and return policies, from empty-box returns to fake delivery claims. ### Score refund risk Assess refund requests using past behavior, device reuse, and outcome history. ### Detect return abuse patterns Block users engaging in empty box returns, item swapping, and excessive INR or SNAD claims. ### Adapt refund policies dynamically Adjust refund timing, eligibility, or friction based on real-time abuse signals. ## Distinguish first-party abuse from legitimate chargebacks Friendly Fraud Identify when cardholders falsely claim fraud on legitimate transactions to recover funds. ### Identify friendly fraud Use account access, device continuity, and payment history to confirm whether the cardholder was involved in the purchase. ### Surface behavioral inconsistencies Detect when checkout, fulfillment, and post-purchase behavior do not line up with a legitimate dispute. ### Increase disputes win rates AI agents help you build stronger disputes cases with evidence aligned to Compelling Evidence 3.0 and other issuer requirements. ## Move from reacting to preventing abuse Multi-Accounting Identify linked identities early so repeat abusers cannot re-enter your platform through new accounts. ### Link accounts and sessions Connect accounts, devices, contact details, and sessions to determine when one user is operating behind multiple accounts. ### Stop coordinated abuse Surface fraud rings and buyer-seller collusion by tracing relationships across users, devices, and payment methods. ### Group linked accounts Collapse multiple accounts tied to the same user in one risk profile using visitor fingerprints and same user scores. ## AI agents that stop
abuse faster, and more accurately Investigate behavior, resolve routine abuse cases, gather evidence, and explain enforcement decisions in real-time. ### Reveal hidden connections across accounts, devices, and transactions to expose coordinated abuse. ### Detect anomalies, recommend rules, and continuously optimize policies for stronger enforcement. ### Resolve entity and ownership mismatches automatically. ### Review and summarize business documents and filings. ## Backed by the industry’s leading agentic risk platform ### Expose repeat abusers Leverage shared risk intelligence rooted in financial and behavioral activity. Data Consortium ### Unravel abuse networks Reveal coordinated abuse by linking customers across accounts, devices, and payments. Fraud Investigations ### Improve accuracy
over time Use outcome-based models to surface early abuse signals and reduce false positives. Machine Learning ### Predict abuse earlier Device and behavioral biometrics distinguish loyal usage from repeat abuse, collusion, and scripted activity that appears legitimate alone. ## Stop policy abuse without impacting legitimate customers ## Extend protection
across the full lifecycle Policy abuse often overlaps with identity and payment risk. Protect your revenue far beyond checkout. ### Identity verification Identify fake and reused identities behind repeat policy violations. ### Card issuing Detect first-party fraud across card transactions. ### Payments Prevent refund and payout abuse tied to risky accounts. ## Frequently
asked questions