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11/07/2026 8:46 pm
TL;DR: First-party fraud now includes item-not-received claims, chargeback abuse, wardrobing, and promo abuse, and Signifyd says it rose 8% in H1 2025 while one in five consumers admit to it, highlighting how hard it is to distinguish fraud from legitimate disputes. The governance challenge is not only detection accuracy but preserving customer experience while proving intent at scale.
NHIMG editorial — based on content published by Signifyd: What is first-party fraud detection and how to combat abuse
By the numbers:
- First-party fraud was up by 8% in the first half of 2025 compared to 2024.
- Shoppers whose orders are falsely declined spend nearly 16% less on subsequent orders.
- First-party fraud, such as INR and SNAD, costs U.S. merchants $50 billion a year, according to Mastercard.
Questions worth separating out
Q: How should merchants reduce first-party fraud without blocking legitimate customers?
A: Use layered risk signals rather than hard rules alone.
Q: Why is first-party fraud harder to stop than stolen-card fraud?
A: Because the person committing the abuse often is the legitimate cardholder or account owner, so normal authentication and checkout controls do not fail in obvious ways.
Q: What signals show that first-party fraud controls are missing abuse patterns?
A: Look for repeated Item Not Received claims, abnormal return rates, promo reuse, linked devices across many accounts, and a rising share of disputes that investigators cannot explain with transaction data alone.
Practitioner guidance
- Instrument post-purchase abuse signals Track refund requests, delivery disputes, return frequency, and chargeback outcomes as first-class fraud events alongside checkout telemetry.
- Correlate identity, device, and account links Build investigation views that tie together device fingerprints, IP history, account reuse, and payment behaviour across transactions.
- Tune controls to reduce false declines Use risk thresholds that consider customer history and transaction context so genuine buyers are not pushed into unnecessary friction.
What's in the full article
Signifyd's full article covers the operational detail this post intentionally leaves for the source:
- The seven fraud patterns in its ecommerce taxonomy, including Item Not Received, SNAD, wardrobing, and bracketing.
- How network analysis uses devices, accounts, and IP addresses to infer intent across disputes.
- The machine-learning workflow behind real-time transaction monitoring and dispute decisions.
- The operational trade-offs between tighter fraud controls and customer experience.
👉 Read Signifyd's analysis of first-party fraud detection and abuse patterns →
First-party fraud: why identity and intent signals matter?
Explore further
11/07/2026 9:13 pm
Identity and intent are now the decisive controls in first-party fraud. Traditional fraud stacks were built to catch stolen credentials, synthetic identities, and obvious checkout anomalies. First-party fraud bypasses those assumptions because the actor often owns the payment instrument and can still be malicious. That makes intent scoring, behavioural context, and network correlation more important than simple approval or decline logic. Practitioners should treat fraud governance as an identity problem that extends beyond authentication.
A question worth separating out:
Q: Who is accountable when dispute and return controls fail?
A: Fraud, payments, customer operations, and risk leadership all share accountability because first-party fraud crosses their boundaries. The programme owner should define how evidence is gathered, how exceptions are approved, and which cases require escalation. Governance fails when every team owns a fragment but no one owns the full customer journey.
👉 Read our full editorial: First-party fraud detection depends on identity and intent signals
