Alexander Mikhailovski, Co-Founder and Chief Product Officer at eComCharge, explains for Payment Expert why payment optimisation should look beyond approval rates to the cost, risk and value of each transaction.
Approval rate is one of the most closely watched metrics in payments. It is easy to understand, easy to benchmark and closely linked to conversion. If one acquiring route approves 90% of transactions and another approves 86%, the first appears to be the obvious winner.
But approval is only an intermediate result. It tells us that the payment went through. It does not tell us whether sending that transaction through this route was the best economic decision.
Payment teams increasingly operate across multiple acquirers and PSPs with different pricing, risk appetites, settlement terms and capacity. Optimising approval rate alone can therefore produce a counterintuitive result: more approved payments, but worse overall economics.
The real question is not simply which route approves more. It is what those additional approvals are worth, and what we have to pay to get them.
The price of four percentage points
Consider a simplified example. A merchant has 10,000 payment attempts with an average amount of €100 and compares two acquiring routes using the same traffic mix.
Route A approves 90% of transactions; Route B approves 86%. That gives Route A 9,000 successful payments and €900,000 of approved volume, compared with 8,600 payments and €860,000 through Route B.
On an approval-rate dashboard, Route A is clearly ahead: 400 additional successful payments, or €40,000 more approved volume.
Now add the economics.
Assume, purely for illustration, that Route A costs 4.2% to process and Route B 2.8%. The resulting processing costs are €37,800 and €24,080 respectively. Route A has generated €40,000 of incremental approved volume, but the fee difference alone is €13,720.
Importantly, that premium is paid across the entire flow sent through Route A, not only on the €40,000 that Route B would have declined.
This still does not make Route A the wrong choice. The extra approvals may justify the premium. But approval rate alone can no longer tell us whether they do.
Suppose expected downstream fraud and chargeback-related losses amount to 1.4% of approved volume on Route A and 0.6% on Route B. That adds another €7,440 to the difference. Choosing Route A for the whole flow now costs more than €21,000 extra before liquidity is even considered.
The question has changed: are the payments recovered by those four percentage points worth what we are paying to recover them?
An approval is not the economic outcome
This matters particularly in iGaming, where approved deposit volume is not the same thing as revenue. A successful deposit puts funds into the player ecosystem, but its economic value depends on what happens afterwards.
The same principle applies elsewhere. In e-commerce, value depends on gross margin. In subscription businesses, lifetime value may matter more than the first transaction. Fraud exposure, chargebacks and reserve requirements can also change the economics after authorisation.
Liquidity is another example. Suppose Route A requires a 10% rolling reserve for 180 days, while Route B holds 3% for 60 days. Those funds are not lost, so treating the reserve itself as an expense would be wrong. But capital has a cost.
If, again purely for illustration, we use an annual cost of capital of 12%, the difference in carrying cost is close to €4,800. Combined with processing and expected risk losses, Route A is roughly €26,000 more expensive to generate €40,000 of additional approved volume.
That works out at about €65 of additional payment-related cost for each extra €100 payment approved by Route A compared with Route B. The €65 is not a fee on that payment. It is the incremental cost of choosing Route A for the full flow, spread across the 400 payments that only Route A approves.
Whether that is expensive or cheap depends on what those extra approvals are worth. If one creates more than €65 of incremental economic value, Route A may still be the better choice. If it creates less, the higher approval rate is destroying value.
The best route depends on the traffic
Aggregate approval rate can also hide where a route’s advantage actually comes from.
Imagine the same two routes processing two traffic segments. For established, trusted customers, Route A approves 93% and Route B 91% – a two percentage point difference. For new or more difficult traffic, Route A approves 87% while Route B approves 75% – a twelve percentage point difference.
If Route A is more expensive or has limited monthly capacity, sending all traffic to it simply because it has the highest overall approval rate may be inefficient.
A better approach may be to send established traffic through the cheaper route, where the approval difference is small, and preserve the premium route for traffic where its incremental value is much higher.
This is where payment optimisation becomes more interesting than simply ranking acquirers from best to worst. There may be no universally best route. A route can be excellent for one transaction and economically inefficient for another.
A high-performing acquiring route is a resource. If it is expensive or capacity-constrained, it should be used where its marginal value is highest.
Approval rates themselves should also be segmented. Saying “Acquirer A gives us 90%, Acquirer B gives us 86%” is not enough if they receive different traffic. The meaningful comparison is like-for-like: the same markets, card profiles, transaction types and risk segments.
In my previous Payment Expert article on primary and secondary traffic, I argued that payment traffic should not be treated as homogeneous because different transactions carry different levels of risk. The same principle applies to performance measurement. A payment route does not have one universally meaningful approval rate. It has approval rates across different traffic segments.
From approval optimisation to economic optimisation
None of this makes approval rate less important. The mistake is turning it into the objective rather than treating it as one of the inputs.
A more mature payment strategy asks three questions: how much additional approval does a route generate for a specific traffic segment; what does that uplift cost once processing, expected risk losses and liquidity are considered; and where is scarce or expensive acquiring capacity most valuable?
The answer does not require a perfect mathematical model. Even an approximate expected-value approach is more useful than automatically maximising a single percentage.
The operational ingredients already exist in many payment stacks: transaction history, traffic attributes, route-level performance data and routing controls. The next step is to connect those signals with the commercial economics of each acquiring channel, so that routing decisions can be judged not only by whether they improve approval, but by whether the improvement creates value.
For eComCharge’s white-label payment software, we see this as a natural next step in the development of platform analytics.
The last percentage point of approval can be the most expensive percentage point you buy. Approval rate tells you whether the payment went through. Economic optimisation asks whether it was worth the price.
