Notifications
Clear all
Topic starter
11/07/2026 8:24 pm
TL;DR: Manual review still has a role in ecommerce fraud detection, but Signifyd cites Merchant Risk Council data showing 35% of fraud teams want to reduce or eliminate it, and says merchants now screen twice as many orders digitally as manually. Human review remains useful for edge cases, but machine-led decisioning is becoming the operational default.
NHIMG editorial — 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 data, a manual review costs $3.47 per transaction on average.
- A review-to-decline rate of less than 10% is a strong indicator to increase automation threshold.
Questions worth separating out
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.
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 do teams get wrong about hybrid fraud controls?
A: Teams often assume that adding more human review automatically improves accuracy.
Practitioner guidance
- Define explicit manual-review thresholds Set objective triggers for when an order moves to human review, such as risk score, transaction value, geography, or mismatch conditions, and document when analysts may override automation.
- Measure review-to-decline conversion Track how often manual reviews end in decline versus approval, then use that ratio to determine whether the team is spending time on decisions that policy could automate.
- Move reviewers into exception management roles Redesign fraud teams so they validate edge cases, tune thresholds, and investigate drift instead of handling every suspicious transaction from first review to final decision.
What's in the full article
Signifyd's full blog covers the operational detail this post intentionally leaves for the source:
- The four-step manual review workflow, including initial assessment, customer verification, historical context, and final determination.
- The cost and customer-experience impact of manual review, including the reported $3.47 average cost per transaction.
- The review-then-decline calculation used to decide whether automation thresholds should be raised.
- The merchant-facing explanation of how machine learning changes decision speed, accuracy, and scale.
👉 Read Signifyd's analysis of manual fraud review versus AI →
Manual fraud review vs. AI: what merchants should change now?
Explore further
11/07/2026 8:54 pm
Manual fraud review is a control for exceptions, not a sustainable trust model. Once review volume becomes routine, human judgment turns into queue management and loses the contextual advantage it was meant to provide. The article shows that the operational question is no longer whether manual review can work, but where it still adds unique value. For practitioners, that means reserving humans for genuinely ambiguous cases.
A question worth separating out:
Q: How do merchants know if automation is ready to replace most manual review?
A: Merchants should look for a low review-to-decline rate, stable model performance, and a small number of well-defined exception types. If most reviewed orders would have been approved anyway, the manual layer is not adding much value. Readiness means the system can make consistent decisions with measurable oversight, not that every case is fully automated.
👉 Read our full editorial: Manual fraud review is giving way to machine-led decisioning
