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12/07/2026 12:18 pm
TL;DR: Fraud cost U.S. companies an average of 3.3% of annual revenue in 2025, while 84% of those losses hit revenue directly or indirectly, according to Sift. The practical lesson is that fraud prevention now has to be treated as a revenue-control and identity-risk problem, not just a detection function.
NHIMG editorial — based on content published by Sift: Get the Fraud Out: How Sift Helps Businesses Stop Fraud Before It Hits Revenue
By the numbers:
- In 2025, U.S. companies lost an average of 3.3% of annual revenue to fraud, and 84% of those losses affected revenue directly or indirectly.
- Sift customers have been able to pair a 99.4% acceptance rate with 80% fewer chargebacks, 37% fewer false positives, and 70% fewer manual reviews.
- Sift says it denied entry to 37.5 million attacks and saw a 122% surge in account takeover attempts in 2025.
Questions worth separating out
Q: How should security teams balance fraud prevention with customer conversion?
A: Use risk-based decisioning rather than broad blocking.
Q: Why do identity and fraud teams need shared controls?
A: Because the same attacker journey often starts with identity abuse and ends with fraud.
Q: What do fraud teams get wrong about automation?
A: They often measure automation by how much work it removes, instead of whether it improves decision quality.
Practitioner guidance
- Unify identity and fraud decisioning Map signup, login, transaction, and post-transaction controls into one policy flow so attackers do not move between blind spots.
- Set latency targets for fraud controls Measure whether automated decisions can act fast enough to change the outcome, not just whether they detect suspicious behaviour.
- Track customer friction as a security metric Include false positives, manual reviews, and approval rates in fraud reporting alongside blocked events.
What's in the full article
Sift's full post covers the operational detail this post intentionally leaves for the source:
- Customer-facing examples of how to reduce chargebacks without increasing false declines.
- The way Sift tunes thresholds, workflows, and manual review handling across the customer journey.
- Specific outcome metrics such as acceptance rate, chargeback reduction, and manual review reduction.
- How Sift combines platform signals with fraud expertise when attack patterns change.
👉 Read Sift’s analysis of how fraud prevention protects revenue without adding friction →
Fraud prevention and customer friction: what teams need to balance?
Explore further
12/07/2026 12:32 pm
Fraud prevention has become an identity governance problem, not just a loss-prevention problem. The article shows that fraud now reaches directly into onboarding, login, and transaction control points. That means the boundary between fraud detection and identity assurance is collapsing in practice, especially where account takeover and credential abuse create the first foothold. Practitioner conclusion: teams should govern fraud as a trust decision across the customer lifecycle, not as a late-stage checkout filter.
A question worth separating out:
Q: How do you know if fraud controls are actually improving?
A: Fraud controls are improving when teams can correlate fewer false handoffs, faster escalation, and better detection of staged attacks across the full user journey. The best signal is not volume of alerts, but whether the organisation can connect identity, device, and behaviour evidence to a defensible decision. If investigations still rely on manual stitching, the model is not mature.
👉 Read our full editorial: Fraud prevention is becoming a revenue control problem
