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12/07/2026 12:16 pm
TL;DR: Fraud teams can misread chargeback rate, block rate, and false positive data when they benchmark without business context, seasonality, or the right timing model, according to Sift’s Blueprint session. The real risk is optimizing one number while missing the fraud, conversion, or customer-friction tradeoff that actually drives revenue.
NHIMG editorial — based on content published by Sift: How to Benchmark Fraud Performance and Find Hidden Gaps
By the numbers:
- 46% of attendees said chargeback rate was their primary KPI, with block rate coming in second.
Questions worth separating out
Q: What breaks when fraud teams benchmark performance without business context?
A: They mistake movement in a metric for movement in risk.
Q: Why do chargeback and block rate KPIs often mislead fraud teams?
A: Because they measure different outcomes, at different points in the customer journey, and often on different time horizons.
Q: How do security and fraud teams know if friction is working?
A: Friction is working when it reduces abuse without creating avoidable abandonment or customer support burden.
Practitioner guidance
- Build cohort-based fraud baselines Split chargeback, block, and false-positive reporting by product line, customer segment, and acquisition channel so the team can compare like with like rather than averaging unrelated risk profiles.
- Use dual reporting models for chargebacks Maintain a fast operational view for weekly decision-making and a lagged transaction-month view for causal analysis, so timing distortion does not drive the wrong control decision.
- Map verification friction to commitment points Place stronger checks after customers have already invested value or effort, then measure whether the control reduces abuse without creating avoidable drop-off at the start of the journey.
What's in the full article
Sift's full discussion covers the operational detail this post intentionally leaves for the source:
- The live session framing behind the benchmarking approach, including how the speakers defined the tradeoffs between growth, fraud loss, and friction.
- The full breakdown of chargeback timing models and why different reporting methods change the numbers leaders see.
- The specific poll results from attendees on chargeback rate, block rate, account takeover rate, and false positives.
- Examples of how teams should think about seasonality and customer commitment points when placing friction.
👉 Read Sift's Blueprint session on how to benchmark fraud performance and find hidden gaps →
Fraud metrics and hidden gaps: what teams should benchmark now?
Explore further
12/07/2026 12:30 pm
Measurement without context creates fraud governance debt: teams that optimise isolated KPIs accumulate blind spots faster than they reduce loss. Chargeback rate, block rate, and false positive rate each tell only part of the story, and each can move in the wrong direction for legitimate business reasons. Fraud operations that do not account for business stage and seasonality end up defending a number instead of defending trust. Practitioners should treat metric design as a governance control, not a reporting afterthought.
A question worth separating out:
Q: Who is accountable when fraud controls create too much friction?
A: Accountability usually sits across fraud operations, IAM, product and customer experience leadership because friction is a governance outcome, not just a tuning issue. If controls are causing avoidable abandonment, the organisation needs ownership for the decision logic, the supporting data and the customer impact. That is why fraud governance must be shared.
👉 Read our full editorial: Fraud benchmarking needs context, not just counts, to reduce loss
