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11/07/2026 1:55 pm
TL;DR: Payment approval percentage measures how many attempted orders clear both issuer authorization and merchant-side approval, and Signifyd says banks falsely decline about 15% of good online orders. The operational challenge is not just fraud reduction but making better decisions on legitimate traffic so revenue is not lost to weak signals, rigid routing, or slow manual review.
NHIMG editorial — based on content published by Signifyd: What is Payment Approval Percentage and How to Improve Your Rate
By the numbers:
- Banks falsely decline about 15% of good online orders, according to Signifyd data.
- Most payment and gateway providers treat 80% as the low end of acceptable for card-not-present transactions and place healthy ecommerce businesses in the 85-95% range.
- A 1% increase in payment approvals can mean 1,000 additional approved orders and $60,000 in recovered monthly revenue at 100,000 attempts with a $60 average order value.
Questions worth separating out
Q: How should security teams reduce false declines without weakening fraud controls?
A: They should improve the quality of the decision inputs before changing the approval threshold.
Q: Why do thin transaction signals increase decline rates?
A: Thin signals leave issuers with little evidence that the transaction is legitimate, so they default to caution.
Q: What do teams get wrong about payment approval percentage?
A: They often treat it as a payment operations metric alone, when it is really a governed trust outcome.
Practitioner guidance
- Separate issuer and merchant decline reasons Break down approval loss into issuer decline, merchant decline, review queue rejection, and routing failure so you can see where legitimate orders are being lost.
- Standardise the identity signals sent with authorizations Send consistent billing, shipping, device, account tenure, and behavioural context with each request so issuers receive a fuller trust picture.
- Tune routing using measured performance by segment Test processor, acquirer, BIN, currency, and geography combinations so routing decisions reflect current performance rather than static defaults.
What's in the full article
Signifyd's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step formulas for calculating payment approval percentage alongside authorization rate and capture rate.
- Operational examples of how routing choices change approval outcomes across card types, geographies, and processors.
- Detailed guidance on pre-authorization screening and manual review reduction workflows that sit behind the revenue claims.
- The article's explanation of how richer issuer signals are packaged to influence approval decisions.
👉 Read Signifyd's guidance on payment approval percentage and false declines →
Payment approval percentage and false declines: what teams need to fix?
Explore further
11/07/2026 2:06 pm
False declines are a trust governance problem, not just a payments optimisation problem. Merchants often treat approval lift as a revenue metric, but the underlying issue is how trust evidence is assembled and interpreted. When billing, device, behavioural, and account-history signals are fragmented, the decision engine becomes conservative by default. For identity teams, that is a reminder that verification quality affects far more than onboarding or login. Practitioner conclusion: govern the quality of trust signals as part of the broader identity and fraud control model.
A question worth separating out:
Q: How do teams know if approval optimisation is actually working?
A: They should measure approval lift alongside false-decline rate, review latency, routing performance, and downstream revenue recovery. A real improvement shows up as more good orders settling without a corresponding increase in fraud losses or chargebacks. If approvals rise but dispute risk also rises, the control change is not working as intended.
👉 Read our full editorial: Payment approval percentage shows where false declines erode revenue
