How to build the business case for payee verification

You may already know why your organisation needs payee verification. Perhaps operations teams are spending too much time investigating failed payments, fraud teams are seeing money sent to unintended recipients, or payments teams want more transactions to reach the right account the first time.

The need is simple: payment details can be technically valid and still point to the wrong recipient. Human error, outdated account data or manipulated beneficiary details can lead to failed or misdirected payments, and once funds move the cost and effort of repair or recovery can increase. Payee verification adds a check before payment initiation, while there is still time to act.

Knowing there is a problem, however, is different from securing investment to solve it.

Management will want to know how large the opportunity is, where the financial return comes from, and whether the expected impact justifies the investment.

That is the role of Know Your Payee (KYP). By confirming that payee details correspond to the intended recipient before money moves, KYP can create value across three areas: operational savings, payment performance and fraud reduction.

A stronger business case puts numbers behind all three.

1. Start with your own payment data

Industry benchmarks can show that payment failures are costly. LexisNexis Risk Solutions, for example, found that the average global fee for each rejected or repaired payment is USD 12, before considering the wider internal effort involved.

But your own numbers will make a stronger investment case.

Start with four questions:

• How many payments or payouts do you process each month?

• What is the average payment value?

• What proportion fail or are returned?

• What does each failed payment cost you to resolve?

A 1% failure rate means something very different to a company processing 10,000 payments a month and one processing one million.

The cost per failure should also reflect more than a transaction fee. We break those costs down further in The True Cost of Failed Payments. For the business case, the next question is how much of that cost earlier payee verification could prevent.

2. The operations ROI: reduce the cost of failed payments

The most direct return often comes from reducing the work created after a payment goes wrong.

Incorrect recipient information can lead to investigation, payment repair, resubmission and support queries. Checking payee information before the payment is sent creates an opportunity to correct a problem earlier.

The Committee on Payments and Market Infrastructures (CPMI) notes that misdirected, rejected or returned payments caused by human error or incomplete information increase the need for manual intervention and costly exception handling. Its 2025 brief on payment pre-validation also notes that checking payment information before initiation can improve straight-through processing and lower operational costs.

The basic ROI calculation is straightforward:

Failures prevented × cost per failed payment = potential operational savings

The model does not need to assume every failed payment disappears. Even a realistic reduction, applied across annual payment volumes, can translate into measurable savings.

One caution: avoid double-counting employee time. If labour is already included in your cost-per-failure estimate, hours saved can demonstrate additional operational capacity, but should not be added again as monetary savings.

3. The payments ROI: recover more successful payment flow

A failed payment does not only create a cost. It can also represent payment value that does not successfully move through your business.

For payment providers, financial institutions and other businesses that earn revenue when transactions complete, recovered payment flow may protect transaction fees, FX revenue or other payment-related income.

This creates a second ROI calculation:

Payment flow recovered × revenue or margin associated with that flow = potential revenue retained

The distinction between payment value and revenue is important. Recovering USD 1 million in payment flow does not mean generating USD 1 million in additional revenue. The business case should apply the organisation's actual fee, margin or take rate.

The same applies to payments that would eventually have been repaired and resent through the same provider. If most failed payments already return, the incremental revenue benefit may be lower and the operational savings may carry more weight.

The value of the model comes from making those assumptions explicit.

4. The fraud ROI: reduce losses before funds move

Fraud teams see a different financial case.

Once a payment reaches a fraudulent or unintended recipient, the organisation may face investigation, recovery attempts, reimbursement or permanent loss. Payee verification creates an earlier decision point by checking whether the account information corresponds to the intended recipient before the payment is sent.

The calculation should focus only on fraud that payee verification can realistically address:

Addressable payment value × fraud exposure × expected reduction = potential fraud losses avoided

“Addressable” matters because payee verification will not prevent every type of fraud. A more credible model isolates the types of fraud where confirming the intended payee can make a difference, then applies a conservative estimate of the potential reduction.

5. Bring the three business cases together

Payee verification can be underestimated when each team looks at only its own part of the problem.

None of these perspectives is wrong. They are different parts of the same business case.

This is visible in real payment operations. One iPiD client found that around 50% of its payment rejections were linked to invalid account details, including incorrect SWIFT information, account details or bank account names entered by customers or internal staff.

Reducing these errors can improve payment success while also reducing the work created for customers and service teams trying to resolve them.

Looking at only fraud losses, failed-payment costs or payment performance can therefore miss part of the return. The fuller investment case considers all three without counting the same benefit twice.

6. Build a model management can challenge

A credible ROI model should show where the numbers come from rather than producing a figure management simply has to trust.

Use internal data where possible. Payment volumes, failure rates, repair costs and fraud losses will usually make a stronger case than generic benchmarks.

Use benchmarks to fill gaps. Where internal data is unavailable, industry figures can provide a starting assumption. Be clear about which inputs are benchmarks so they can be replaced later.

Test different scenarios. A conservative, expected and higher-impact case can show how the economics change if fewer failures are prevented or fraud reduction is lower than expected.

If the investment still makes sense under conservative assumptions, the case becomes much easier to defend.

From payee verification need to investment case

The business case for payee verification is not simply that failed payments and fraud are expensive. The more useful question is what earlier intervention could be worth within your own payment flow.

That means bringing together three sources of value:

1. Failure-cost savings from preventing avoidable payment errors

2. Revenue associated with recovered payment flow when more transactions complete successfully

3. Fraud losses avoided by identifying recipient risk before funds move

Know Your Payee (KYP) provides the pre-payment layer to check recipient information while there is still time to act. iPiD enables organisations to apply those checks before funds move, helping close the gap between identifying a payment problem and preventing it.

The next step is to replace industry averages with your own numbers.

Use the iPiD Payment Verification Savings Calculator to estimate the potential value across failed payments, recovered payment flow, and fraud reduction.