You’re asked to explain why authorization rates dropped 2.3% last week. Was it an issuer-side problem, an acquirer issue, or just a reporting delay?
You pull data from three PSP dashboards, an acquirer report, and last month’s performance spreadsheet. Nothing quite lines up.
You’re still expected to give a clear answer on payment performance. But your data lives in silos, updates on different schedules, and uses different definitions for the same metric.
In day-to-day payment operations, this friction shows up as very real drag:
Manual reporting across multiple providers
Delayed or incomplete data from PSPs and acquirers
KPIs tracked in isolation, without context or comparability
Tracking the right KPIs only matters if you can slice, compare, and act on them quickly. And that’s something most in-house stacks struggle to deliver at scale.
KPI #1: Chargeback Ratio
You already track chargebacks. But knowing how many you have doesn’t tell you why they’re happening, or where to intervene. Chargebacks tracked in aggregate are a lagging indicator. By the time they spike, revenue damage has already started.
According to Mastercard’s 2025 State of Chargebacks report, drawing on research from Datos Insights, global chargeback volumes are projected to increase by 24% between 2025 and 2028, reaching 324 million transactions per year.
What actually moves the needle is breaking chargebacks down with intent. Beyond the headline ratio, here’s what you should track:
Chargeback ratio by acquirer, region, currency, and more—not just in aggregate
Fraud-related chargebacks split by card type, issuer, country, or other specific terms
Trends over a specific short-term time period or a set number of transactions, not month-end snapshots across PSPs
If Acquirer A shows a 0.6% fraud-driven chargeback ratio and Acquirer B sits at 1.1%, the problem isn’t “rising fraud.” It’s routing, rules, or issuer behavior tied specifically to Acquirer B.
KPI #2: VAMP Ratio
VAMP (Visa Acquirer Monitoring Program) ratio measures how close you are to triggering Visa monitoring thresholds based on fraud and dispute activity. In plain terms, it’s Visa telling your acquirer whether you’re becoming a cost and risk problem.
Again, monitoring this ratio closely with proactive alerts over shorter windows of time and with specific parameters helps you get ahead of problems before it’s too late.
Let’s say your overall VAMP ratio looks stable, but one acquiring bank shows a steady rise in fraud-driven disputes. Your VAMP ratio quietly creeps up week over week. By the time your acquirer flags you for increased monitoring and higher fees, there’s no warning and no time to course-correct.
The problem? Most merchants only see VAMP after the damage is done, buried in scheme reports or flagged by an acquirer weeks later. By then, you’re reacting under pressure.
A rising VAMP ratio in France with acquirer A when processing Visa cards from a specific issuer is an early warning signal. A rising VAMP ratio in aggregate across your PSPs, regions, and card mix means you're too late. You’ve likely already triggered higher fees and/or stricter terms from your acquirer.
With IXOPAY’s AI Payment Intelligence Platform, you can track your VAMP ratio continuously, slice it by acquirer or a number of other parameters, and spot trends before thresholds are breached. It turns scheme risk from a nasty surprise into something you can actively monitor and manage.
KPI #3: Authorization Rate
Your authorization rate measures how often issuers approve the transactions you send them. Even a 2-3% drop translates directly into lost revenue at checkout.
What matters is where and why approvals fail. Which issuers, acquirers, and decline codes are driving the drop? Most merchants track this KPI too late and too broadly, without isolating root causes.
Tracking authorization rate with precision:
By acquirer and issuer BIN: A single underperforming acquirer can drag down global performance.
By decline reason code: “Insufficient funds” vs. “Do not honor” demand very different fixes.
By geography and payment method: Issuer behavior varies sharply by market and wallet.
For example, if your overall auth rate is 92% but falls to 85% on one acquirer for EU-issued cards, the failure point might be routing, rules, or issuer messaging. Platforms like ours let you spot these patterns early and act before revenue quietly leaks away.
KPI #4: Decline Rate
Your decline rate shows how many transactions fail, but on its own, it functions only as a loss report. The real lever is why those payments were declined.
Decline reason codes tell you whether a payment is recoverable or dead-on-arrival. Treating all declines the same can leave revenue on the table.
Here’s what to track in practice:
Soft vs. hard declines: Soft declines (temporary limits, issuer timeouts, authentication issues) are often recoverable with smart retries. Hard declines (invalid card, closed account) are not.
Decline reason accuracy: Misclassified declines lead to failed retries, by triggering retries that shouldn’t happen or preventing ones that would have converted.
Retry outcomes by reason code: Which decline reasons actually recover on a second or third attempt?
Example: If a spike in “Do Not Honor” declines is really issuer-side friction on one acquirer, blind retries won’t help. But adjusted routing, timing, or authentication might.
Knowing why a payment failed lets you retry intelligently, tune acceptance flows, and recover revenue instead of merely documenting losses after the fact. Platforms like our AI Payment Intelligence solution surface decline reasons in real time, so recovery becomes a control loop, not a post-mortem.
In Conclusion: From Metrics to Momentum
Individually, these KPIs tell you what broke.
Together, they can tell you where revenue is leaking and how to fix it.
As any payment professional can tell you, the real challenge isn’t a lack of data. It’s that PSPs deliver it in different formats, on different schedules, with different schemas. That fragmentation costs teams hours reconciling reports, makes fair comparisons nearly impossible, and surfaces KPIs too late to act.
Our AI Payments Intelligence solution solves this by standardizing payment data across providers, so you can analyze performance in one place without stitching together raw files or fragile pipelines. The result is faster insight, sharper decisions, and measurable revenue recovery.