Topic starter
12/07/2026 12:16 pm
TL;DR: Fraud benchmarking can mislead teams when KPI performance is read without business context, seasonality, or the right timing model, Sift details. Chargeback, block, and false positive rates need a measurement model that matches how losses settle, or controls can look healthier than they are.
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: 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.
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.
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.
Glossary terms: Fraud Benchmarking, Chargeback Timing Model, False Positive Rate, Customer Friction
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This topic was modified 7 hours ago by NHI Mgmt Group
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