What should airlines do when manual review starts slowing down approvals?

Why manual review should become an exception path, not the default gate

manual review is useful when the transaction is unusual, high-value, or carries a higher likelihood of fraud, chargeback, or account takeover. It becomes counterproductive when it is used as a routine bottleneck for everything. At that point, the team is spending analyst time on low-yield cases instead of concentrating on the few decisions where delay and scrutiny actually reduce loss.

A better operating model is to let automated scoring handle the bulk of approvals and reserve human review for cases that cross a defined risk threshold. That threshold should reflect the cost of delay, the expected fraud exposure, and the rate of false positives, not simply how uncomfortable the organisation feels about automation.

How to decide which cases still deserve human review

The right question is not whether a person can inspect a booking, but whether the extra scrutiny changes the outcome enough to justify the delay. If a rule consistently routes ordinary transactions into review, the queue will expand faster than the risk signal improves. Airlines should separate high-impact exceptions from low-value friction and use different treatment for each.

• Review only when the financial exposure, loyalty abuse risk, or account-risk signal is materially elevated.
• Automate clear approvals where the incremental security value of waiting is small.
• Track whether reviewed cases actually produce better decisions than automated approval would have produced.

This is especially important when the approval process affects customer experience. A long queue can create abandoned bookings, call-centre spillover, and inconsistent decisions between agents, which weakens both operational efficiency and control quality.

What slows approvals in practice, and why that matters

Queues usually slow down because the review criteria are too broad, the evidence presented to analysts is incomplete, or the team lacks a clear decision rule for edge cases. When every exception looks urgent, reviewers are forced to investigate too many low-signal cases, and the programme starts measuring activity instead of effectiveness.

That creates a control weakness: the organisation assumes manual review is reducing loss, but it may only be reducing throughput. For this kind of process, the key metric is not how many tickets are reviewed, but whether the review stage is changing approval quality enough to justify its operational cost.

Risk and Threat Considerations

Manual review can become an attractive target for abuse if attackers learn which transactions are likely to be slowed, escalated, or inconsistently handled. Overly broad review rules can also create avoidable operational risk by delaying legitimate bookings while still missing the small set of cases that matter most.

Failure mechanism: The review queue absorbs too many low-risk transactions, which increases latency, dilutes analyst attention, and makes the control less effective at catching genuinely risky activity.

Impact: Approval times rise, abandonment and customer friction increase, and the organisation may end up paying for a control that adds cost without materially lowering fraud or account-risk exposure.

Practitioner Guidance

What to measure: Separate queue volume, average time-to-decision, and fraud or loss reduction. If review time is rising while prevented loss stays flat, the control is likely oversized for the risk it is catching.

Decision rule: If a case is not materially more expensive to approve than to investigate, keep it in the automated path and reserve human review for exceptions with a clear downside if approved incorrectly.

Common mistake: Treating manual review as proof of caution. In practice, a large queue often means the policy is too blunt, not that the programme is safer.

Practitioner takeaway: The objective is to make human review scarce, fast, and decision-rich, because once it becomes a blanket backstop, it stops being a risk control and starts becoming a throughput problem.

Related resources from NHI Mgmt Group

• How should airlines automate fraud review without slowing down legitimate bookings?
• When should organizations review their NHI policies?
• When should organizations review access controls?
• When should enterprises review their extension policies?