Topic starter
11/07/2026 8:24 pm
TL;DR: Merchants now screen twice as many orders digitally as manually, and 35% of fraud teams want to reduce or eliminate review, according to Signifyd. Fraud operations should reserve analysts for ambiguous cases while automation handles routine decisions.
Editorial analysis by NHI Mgmt Group, based on content published by Signifyd: “Manual fraud review vs. AI: Best practices for merchants”.
By the numbers:
- 35% of fraud management teams cite reducing or eliminating manual fraud review as an area for improvement, according to Signifyd.
- A manual review costs $3.47 per transaction on average, according to Signifyd.
- A review-to-decline rate of less than 10% is a strong indicator to increase automation thresholds, according to Signifyd.
Key questions
Q: Why does manual fraud review become expensive at scale?
A: Manual fraud review becomes expensive because each case consumes analyst time, adds delay to order fulfilment, and increases the chance of inconsistent decisions.
Q: What are the signs that fraud review thresholds are too low?
A: A low review-then-decline rate is a strong sign that many cases sent to analysts could have been handled automatically.
Q: How should merchants reduce manual fraud review without increasing fraud risk?
A: Merchants should first segment transactions into stable, low-risk decisions and true exceptions.
Practitioner guidance
- Define review as an exception workflow Set explicit criteria for when a transaction leaves automated decisioning and enters human review, such as unusual value, mismatched identity signals, or high-risk geographies.
- Track review-to-decline performance Measure how many reviewed orders end in a decline and use that ratio to decide whether the automation threshold should move higher or lower.
- Use model managers, not manual clerks Recast fraud analysts as model managers who tune thresholds, monitor drift, and investigate the cases that automation cannot resolve with confidence.
Bottom line: Manual fraud review still has a valid role, but only as a bounded exception path when automated decisioning cannot confidently resolve a suspicious order.
Glossary terms: Manual Fraud Review, Machine-Led Decisioning, Review-to-Decline Rate
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This topic was modified 8 hours ago by NHI Mgmt Group
11/07/2026 8:54 pm
Manual review fatigue is a governance problem before it is a staffing problem. When fraud teams stay too dependent on human adjudication, the control stops scaling with transaction volume and becomes a bottleneck for both risk and revenue. The article’s central signal is that merchants are already moving toward digital screening because the exception workflow has outgrown the exception rate. Practitioner takeaway: design the operating model so humans handle edge cases, not the main line.
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: When should human review still be part of fraud decisioning?
A: Human review still makes sense when transactions are unusual, high-value, or contain conflicting signals that require context a model may not capture well. It is best used as a bounded exception path, not as the main decision layer. The goal is to preserve expert judgment for edge cases, not routine approvals.
👉 Read our full editorial: Manual fraud review is giving way to machine-led decisioning