The Nuance of Approval Rate Optimization

Most merchants, even those not yet utilizing their comprehensive payments data, care about approval rate. This makes perfect sense, as more approved transactions should translate into more revenue for your business. Except it turns out it’s more nuanced than that. 

We've written about this before, but it’s clear the industry is still wrestling with the complexities of approval rate. The MRC’s 2026 Global eCommerce Payments & Fraud Report offers a clear signal about this: the merchants surveyed ranked payment success rate at the top of their monitored metrics, with authorization rate (i.e. approval rate) coming in sixth. Payment success rate captures what actually made it all the way through checkout, while authorization rate only tells you what got approved at one step in the process. Merchants ranking payment success over approvals is a quiet acknowledgment that approval rate alone doesn't give you the full picture.

Understanding why approval rate can't be your only target starts with looking at what optimizing for it actually does to the rest of your payments metrics.

Approval Rate Optimization Considerations

You’re likely already familiar with the tradeoffs between approvals and fraud; if you relax your fraud rules to let more transactions through, your approval rate will go up, but so will your chargebacks. You've optimized one metric at the direct expense of another. But there are subtler ways approval rate can mislead you:

Approval Rate Math

How you calculate approval rate matters. For example:

• Are you measuring gross or net approval rate? 

• Does your calculation include traffic that was blocked by your fraud provider before it ever hit authorization? 

• Are retries counted separately or deduped into a single attempt?

If you and your PSP are calculating approval rate differently, you may be optimizing toward a number that doesn't mean what you think it means. This is likely why merchants from the MRC survey mentioned at the beginning of this post consider payment success rate the more complete metric: it captures the full journey from checkout to settled transaction, including all the places a payment can fall apart that authorization rate never sees.

Given the inherent flexibility in how approval rate is defined and calculated, you may have internal teams measuring the same metric in different ways. If your payments, fraud, and marketing teams are all working towards an “optimized” approval rate, it's worth getting clear on exactly what's being measured: what traffic is included, how retries are handled, and where fraud blocks fall in the calculation. Small differences in methodology can produce very different numbers.

Retry Costs

Retrying failed transactions is a logical and effective tactic for driving up deduplicated approval rates. If your retry logic is aggressive enough, you can recover good transactions that should have succeeded, thereby driving revenue and delighting customers. But if the cost of those retries approaches or exceeds your margin, there's no net revenue gain. This becomes especially problematic if the card brands consider your retry strategy excessive and slap you with penalty fees. We call this the ROI sweet spot; learn more in our blog post on the subject.

False Declines

Loosening fraud rules to improve approvals means inevitably letting more fraud in. More fraud leads to higher costs in terms of chargebacks, dispute-related fees, and operational time spent fighting back. Furthermore, you risk issuing banks labeling your business as risky, thereby hurting your long term performance.

On the other hand, tightening fraud rules to reduce chargebacks can mean blocking legitimate transactions. Often referred to as “false declines,” these unnecessary transaction failures represent a huge liability for your revenue and reputation with customers. The 2026 MRC survey found that 38% of merchants have a false positive rate of 2-5%, and 13% have a rate of 10% or higher. If you run a recurring or subscription business especially, these false declines can translate into millions of dollars lost in customer lifetime value from legitimate customers.

Neither extreme is the answer, and optimizing approval rate without tracking both sides of that equation gives you an incomplete picture.

Alternative Payment Methods

Alternative payment methods (APMs), including digital wallets, BNPL, virtual cards, and other non-traditional instruments, now make up a meaningful share of transaction volume for many merchants. Because APMs don’t behave like traditional cards, aggregated approval rate metrics across all accepted payment methods can hide important details from your payments team. 

Data presented at MRC in Vegas last month by representatives from CMSPI and T-Mobile showed that APMs can underperform traditional payment methods significantly, particularly for merchant-initiated transactions (MITs) where the gap can be over 50%. If your overall approval rate looks healthy but APMs are dragging down performance in specific segments, you won't see it unless you're looking at BIN-level data. This matters more as APM adoption grows; if you aren't analyzing performance at the BIN level, you’re essentially flying blind on an increasingly large portion of your volume.

What You Should Actually Be Optimizing For

Approval rate is best understood as one signal in a larger system, not a standalone target. The merchants who are making real progress on optimization are tracking approval rate alongside fraud rate, false positive rate, retry costs, and chargeback rate simultaneously. In other words, payments optimization and approval rate optimization aren’t the same thing.

To really tackle approval growth in a way that works for your entire business requires data infrastructure that lets you see all of those metrics together, segmented by processor, payment method, BIN, and more. Only then can you identify when something moves, why, and what it might be costing you somewhere else.

Want to see how your approval rate stacks up across processors and payment methods? Talk to us.