Ask a payments team for their payment approval rate, and you will get a number within seconds. Ask how much of the gap to 100% they can realistically close, and the answer takes much longer. That second question matters more, because approval rate turns the traffic you have already paid for into revenue.It is also the metric companies now hire for. When we analyzed 112 Payment Manager job descriptions across 15+ countries for Corefy's report on the state of the Payment Manager role, approval rate was the most frequently named KPI, ahead of payment success rate and processing cost.In my previous article for Financial IT, I looked at the anatomy of a declined transaction and why two failures that look identical to a shopper can need completely different fixes. This piece covers the other side of the same coin: how payment teams win more approvals, and in which order to pull the levers. After years of reviewing client payment setups in customer success at Corefy, the pattern I see most often is teams with the right tools aimed at the wrong segment.Agree on which approval rate you are improvingThree metrics get used interchangeably, and mixing them is the fastest way to report progress that is not there.Authorization rate: approved authorization requests as a share of all requests sent to issuers. Visa, for example, calculates authorization rate on the first attempt of a unique transaction, so retries do not inflate it.Payment acceptance rate: transactions accepted for processing as a share of everything submitted, including payments stopped before they reach the issuer by your own fraud rules, 3D Secure (3DS), or technical errors.Payment success rate: payments completed end to end as a share of payment attempts started at checkout.The job-description data shows how fragmented this vocabulary is. Approval rate, payment success rate, acceptance rate, and authorization rate all appear as separate KPIs, often describing overlapping things. If your product team, finance team, and PSP account manager each quote a different number, they are probably measuring different stages of the same funnel.Pick one primary definition, write it down, and report it in two views. First-attempt approval rate shows the quality of your setup. Intent-level approval rate counts each checkout or renewal once and shows how many customers eventually got through. When intent-level approvals rise while first-attempt approvals stay flat, retries are doing the work, and the underlying setup has not improved.Find where your approval headroom actually sitsA blended payment approval rate averages segments that behave very differently. A 90% overall figure can combine 97% on returning domestic customers with 70% on one issuer country routed through a foreign acquirer. The second segment is where your uplift lives, and the blended number hides it.Before changing anything, break approvals down by the dimensions issuers themselves react to:Issuer country and BIN rangeProvider, MID, and acquiring countryPayment method and card type: credit, debit, prepaid, commercialCustomer type: new or returning, customer-initiated or merchant-initiatedAmount band and currencyThen rank each segment by its volume multiplied by the gap to your best-performing comparable segment. That ranking becomes your backlog. In the approval reviews we run with clients, this single exercise usually turns a vague goal such as 'lift approvals by two points' into three or four concrete hypotheses about specific issuers, corridors, or MIDs.How to improve payment approval rate: five levers that workOnce you know where the headroom is, the levers below cover most of it. I have ordered them by how often they deliver measurable uplift in the multi-provider setups I work with.1. Route each transaction to the provider most likely to approve itThe same card can be approved by one acquirer and declined by another, because each has different issuer relationships, risk appetite, and acquiring geography. For merchants already working with several providers, routing based on segment-level performance, rather than cost or a fixed priority list, is usually the biggest lever.Good routing uses the attributes from your segmentation: BIN, issuer country, amount, currency, and customer history. It can also bind a card to the MID that last approved it, so returning customers keep landing on a path that works for them. Your payment team should be able to change and test the routing logic in hours, not in a release cycle. That is the principle behind smart payment routing and cascading in Corefy, where teams compare approval rate, cost, and latency across routing strategies before rolling one out.2. Make cross-border transactions look domesticIssuers treat card-not-present transactions acquired abroad more conservatively, partly because fraud rates on cross-border card transactions substantially exceed domestic ones. If your segmentation shows a persistent gap on a specific corridor, adding a local acquiring route in that market is often worth more than any amount of retry tuning. Local payment methods do the same job in markets where cards are not the default.The trade-off is operational. Every local acquiring relationship adds contracts, onboarding, reconciliation, and reporting, so prioritize corridors by volume and approval gap instead of expanding everywhere at once.3. Send issuers more and better dataIssuers approve what they can assess. A missing billing address, an inconsistent descriptor, or an outdated card credential can push risk models toward a decline. Network tokens are the clearest example of data quality paying off: Visa reports a 4.8% higher authorization rate for tokenized card-not-present transactions compared with raw card numbers, based on its 2025 network data. Tokens also update automatically when a card is reissued, which matters for subscriptions and any stored-credential traffic.Card schemes increasingly reward richer data sharing, too. Visa's Data-driven Commerce Acceptance program is one example. Check with each provider which data fields they actually pass to the issuer. A field you collect at checkout but your acquirer drops does nothing for approvals.4. Apply authentication where it adds approvals3DS is a trade-off between issuer confidence and customer drop-off. Challenging every transaction protects liability but loses customers mid-flow; skipping it where issuers expect it triggers soft declines. The approval-positive approach is to request exemptions where your acquirer supports them, send full 3DS data so more transactions pass frictionlessly, and reserve step-up challenges for genuinely ambiguous cases.Apply the same logic to your own fraud rules. Merchant-side blocks never reach the issuer, so they do show up in your authorization rate, yet they cut your payment acceptance rate directly. Review false positives as often as you review fraud losses, or your rules will drift towards blocking more because it feels safer.5. Give recoverable payments a second routeSome first attempts fail for reasons unrelated to the customer: a provider timeout, one acquirer's risk engine, a temporary issuer outage. Cascading sends the payment to the next eligible provider without the customer re-entering details, which turns a would-be failure into an approval. Keep it disciplined. Cascade only on genuinely recoverable responses, follow scheme retry rules, and track recovered approvals separately so they don't mask a weak first-attempt rate.Why more providers do not automatically mean more approvalsAdding providers creates options. Approvals rise only when you can steer traffic between them. Corefy's 2025 Payment Maturity Report, based on 672 questionnaires from businesses worldwide, shows the market moving fast on the first part: 37.1% of respondents now work with five or more providers, up from 24.6% a year earlier. Yet only 7.8% described their core setup as a payment hub with routing and cascading, and 58.5% still operate at the fragmented stage, with providers connected but not managed as one system.That gap shows up in daily work. Our Payment Manager role report found that routing decisions are still often made manually, based on experience rather than real-time data, and that approval rates across PSPs are frequently reconciled by hand because each provider returns different error codes. A team comparing providers in spreadsheets cannot route by BIN in real time.A fair counter-argument: not every business needs this layer. A merchant selling in one country through one strong acquirer will get more from network tokens, clean data, and 3DS tuning than from adding providers. The case for orchestration starts when you already have several providers and cannot act quickly on the performance differences between them.Turn approval rate into an ongoing programApproval uplift decays when nobody owns it. Issuers update their risk models, providers change performance, and new markets bring new BIN ranges. According to the maturity report, only 41% of businesses have a dedicated payment manager or payments department; the rest handle payments as an administrative task or a side responsibility.The teams that sustain their gains share three habits:A weekly review. First-attempt and intent-level approval rates by provider, corridor, and top BINs, with alerts for sudden drops.Every change treated as a test. Each routing or rule change gets a hypothesis, a control group, a success metric, and a rollback plan.A value estimate on every backlog item. Approval work then competes fairly with the rest of the product roadmap.The value estimate is simple arithmetic. At $10 million in monthly card volume, every percentage point of approval rate is worth roughly $100,000 in additional processed revenue each month, before margin. To model this for your own stack, including the effect of routing and cascading, try Corefy's payment orchestration ROI calculator.Where to startPayment approval rate is the most direct link between your payment stack and your revenue, and most of the headroom usually sits in a handful of segments. Start by agreeing on one definition and separating first-attempt from intent-level approvals. Segment until you find the issuers, corridors, and MIDs with the largest gaps. Then work through the levers in order of expected value: routing, local acquiring, data quality, authentication, and a second route for recoverable payments.The goal is a setup where every transaction gets its best available chance of approval on the first attempt, and a sensible second chance when it needs one. No#PaymentOptimization Alexandra PotapskaHead of Customer SuccessCorefy30 Sep, 2026