Topic starter
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.
Editorial analysis by NHI Mgmt Group, 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.
Key questions
Q: What are the signs that fraud benchmarking is not giving teams a reliable view of performance?
A: Warning signs include teams relying on isolated metrics, inconsistent review processes, or benchmarks that are difficult to compare across channels.
Q: Why do chargeback rates look different depending on the measurement model?
A: They differ because the loss can be tied to the transaction month, the current month, or the previous month depending on the model.
Q: What are the signs that a fraud programme is optimising the wrong metric?
A: A common sign is that one KPI improves while executives still complain about blocked customers, support tickets, or missed abuse patterns.
Practitioner guidance
- Benchmark against business stage and seasonality Define internal baselines for low, normal, and peak activity so chargeback and block trends are read against the right demand pattern, not a single monthly average.
- Use more than one chargeback timing model Compare a fast operational view with a settled attribution view so the team can distinguish immediate fraud pressure from delayed loss recognition.
- Track false positives alongside fraud loss Add legitimate-customer friction to the same review cycle as fraud loss so the team can see when controls are protecting revenue versus suppressing it.
Bottom line: Fraud programmes can look healthy on paper while still missing the business tradeoffs that actually matter, especially when leaders read raw rates without operational context.
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12/07/2026 12:30 pm
Fraud benchmarking is really control calibration, not reporting hygiene. The article shows that a team can have plenty of data and still make poor decisions if it does not understand what each number is actually measuring. That is a governance problem, not a dashboard problem. Practitioners should treat benchmarking as a control-design exercise that links fraud loss, false positives, and conversion into one decision model.
A few things that frame the scale:
- Nearly 60% of companies reported that fraud losses were still increasing in 2025.
A question worth separating out:
Q: How should fraud teams balance chargeback prevention with customer experience?
A: By making precision a governance goal, not only a model metric. Teams should validate dispute rules against customer friction, monitor repeat-purchase impact, and use real-time evidence to intervene only where the risk is clear. The best programmes stop abusive behaviour without turning routine disputes into a service failure.
👉 Read our full editorial: Fraud benchmarking needs context, not just counts, to reduce loss