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11/07/2026 2:02 pm
TL;DR: Payment optimization aims to increase the share of legitimate ecommerce orders that complete checkout without raising fraud risk, and Signifyd says approval rates, bank declines, technical failures and false fraud blocks are the main pressure points. The governance challenge is not just conversion loss, but the control design choices that decide which good customers are turned away.
NHIMG editorial — based on content published by Signifyd: Payment Optimization Guide: 6 Strategies for Ecommerce Growth
By the numbers:
- According to Signifyd’s State of Commerce 2025 Report, 19% of shoppers who are turned away without a clear reason abandon the transaction and shop with another retailer instead.
- Research shows that 42% of U.S. customers abandon purchases if their preferred payment method isn’t available.
- Many ecommerce merchants aim for a healthy payment approval rate in the 85% to 95% range.
Questions worth separating out
Q: How should security teams reduce false declines without weakening fraud controls?
A: Start by separating hard fraud stops from soft operational failures, then improve the context used in payment decisions.
Q: Why do legitimate payments get blocked even when fraud risk is low?
A: Legitimate payments can be blocked when issuers have too little context, when the payment path fails technically, or when merchant fraud rules are too rigid.
Q: What do merchants get wrong about payment fraud controls?
A: The common mistake is assuming stricter controls always improve outcomes.
Practitioner guidance
- Separate decline types in reporting Classify failures as method unavailability, technical timeout, issuer decline, or merchant-side false decline so teams can fix the correct control instead of averaging all declines into one metric.
- Measure approval quality, not only volume Track approval rate, issuer authorization rate, retry completion, and repeat-customer decline rate to identify where good orders are being blocked.
- Preserve transaction context across the flow Pass device, IP, customer history, and order score consistently between checkout, risk decisioning, and issuer handoff so legitimate variation is less likely to look suspicious.
What's in the full article
Signifyd's full article covers the operational detail this post intentionally leaves for the source:
- The payment-flow examples behind each decline type, including customer preference gaps, bank declines, and merchant-side fraud blocks.
- The step-by-step strategies for improving approval rates without loosening fraud controls.
- The operational examples showing how issuers react when merchants send cleaner traffic and richer context.
- The practical recovery tactics for soft declines, retries, and alternate payment paths.
👉 Read Signifyd's payment optimization guide for ecommerce growth →
Payment optimization in ecommerce: what merchants are missing?
Explore further
11/07/2026 2:12 pm
False decline governance is a trust problem, not just a conversion problem: when merchants optimize only for stop-loss behaviour, they often create a parallel loss in legitimate revenue. Payment decisioning works better when fraud, context, and customer history are evaluated together rather than in isolated control silos. Practitioners should treat decline quality as a governance metric, not only an ecommerce KPI.
A question worth separating out:
Q: Who is accountable when payment optimization causes revenue loss?
A: Accountability usually sits across fraud, payments, product, and risk teams because payment outcomes depend on decisions made in all four areas. If approval rates are weak or repeat customers are being blocked, the issue is usually governance, not one isolated system failure. Teams should assign ownership for decline quality and recovery performance.
👉 Read our full editorial: Payment optimization exposes the governance gap in ecommerce checkout
