DEV Community: Vladyslav Kolodistyi

The Conversion Case for Passkeys: Vladyslav Kolodistyi on Removing Friction From Card Payments

Vladyslav Kolodistyi — Wed, 14 Oct 2026 11:00:00 +0000

Four numbers from the FIDO Alliance explain why I now pitch passkeys to merchants as a payments revenue project rather than a security one:

93% sign-in success with passkeys, against 63% for other methods.
8.5 seconds to sign in with passkeys, against 31.2 seconds for email codes, SMS and similar options.
73% less time spent on sign-in overall.
Up to 81% fewer login-related help desk incidents.

All four come from the FIDO Alliance Passkey Index, which pools real deployment data from Amazon, Google, Microsoft, PayPal, Target, TikTok and others. They measure sign-ins and authentication in general rather than card payments specifically. But the mechanics that cause those gaps — typing codes, switching apps and waiting for messages — are exactly the mechanics that cost merchants card payments at checkout every day.

I'm Vladyslav Kolodistyi. This article is the business case for passkeys and biometrics in payments, written for the people who own conversion targets rather than security budgets.

Where card payments leak at checkout

Picture the last step of an online card purchase in Europe. The customer has chosen a product, entered delivery details and typed the card number. Then the issuer asks for strong customer authentication on the card payments. The page redirects. A banking app opens, or a text message is promised. The customer waits, switches apps, finds the code, types it, returns. Some customers do all of that for their payments without complaint. Many do not. The phone is in another room, the code arrives late, the banking app wants an update, the session times out. Each of those small failures turns a finished basket into an abandoned one, and none of them shows up as "fraud prevented". They show up as lost card payments that nobody can recover.

Merchants rarely see this leak in card payments clearly, because it hides between systems. The payments provider sees a failed authentication and no biometrics option to fall back on. The analytics tool sees an exit from the checkout page. The issuer sees authentication that was never completed. Nobody owns the full picture of those payments, so nobody fixes it.

“The most expensive step in card payments is often the one the merchant does not control and cannot see.”
Vladyslav Kolodistyi

What passkeys change, in the words of Vladyslav Kolodistyi: they replace that whole detour with one biometrics prompt inside the checkout the customer already trusts. The device shows the amount, the customer looks at the screen or touches the sensor, and the signed authentication goes to the issuer in the background.

Payment authentication as a growth lever

Once payment authentication becomes a single gesture with passkeys, the maths of payments changes. To make that concrete, here is an illustrative model. It is not a forecast for any real merchant, only a way to see how success rates at the authentication step flow through to revenue from payments. Model by Vladyslav Kolodistyi:

Twelve extra points of success on a single step add 720,000 euros a month in this example. Even if a real merchant captures a fraction of that, the gain in payments dwarfs the cost of adding passkeys to checkout. And the benefit repeats every month, for every returning customer who enrols.

A second gain, noted by Vladyslav Kolodistyi: it is quieter, but real. SMS codes cost money on every attempt, including failed ones. Support teams field calls about codes that never arrived. Passkeys remove both costs for payments teams. For merchants and payments providers with large volumes, the savings on messaging and support alone can fund the rollout.

Why biometrics make shoppers say yes

Conversion only rises if customers actually enrol in passkeys. Here biometrics do the heavy lifting. Customers already unlock their phones with a face or fingerprint dozens of times a day. Approving card payments with biometrics feels natural, not technical. The FIDO Alliance guide to passkeys makes the point that the biometrics check happens on the device and never reaches the website, which answers the privacy question most shoppers have.

Enrolment timing for passkeys matters as much as the technology. The best moment to offer passkeys is right after a successful purchase, when the customer feels good about the brand. A prompt that says "Pay faster next time with your face or fingerprint" converts far better on the confirmation page than in account settings. Uber and TikTok have both reported that the vast majority of their passkeys came from well-placed prompts in the main flow.

“Ask for passkeys at the moment of successful payments. A happy customer enrols. A frustrated one closes the tab.”
Vladyslav Kolodistyi

Returning customers bring the biggest gains in payments, because they are the ones who would otherwise face authentication again and again. A merchant with a loyal base can move a large share of card payments to passkeys and biometrics within months, and every one of those payments skips the old detour.

Mobile first, but not mobile only

Most card payments now start on a phone, and that is where passkeys shine. The phone holds the key and the biometrics sensor in one device, so payments authentication takes a single glance. On mobile, I expect passkeys to become the default for returning customers within a few years.

Desktop checkout needs more thought. Many laptops have fingerprint readers or face recognition, and those work just as well. Where they do not, the customer can approve payments by scanning a QR code with a phone that holds their passkeys, then confirming with biometrics on the phone. It adds a step, but it still beats waiting for an SMS code.

Tip from Vladyslav Kolodistyi: test both paths with real customers before launch, and measure authentication success for payments on each device type separately.

“The phone is already the customer's wallet, keyring and ID. Making it the authentication device for payments is the obvious next step.”
Vladyslav Kolodistyi

What to measure

If you run passkeys as a revenue project, measure it like one. Track authentication success and time to complete separately for passkeys and legacy methods across all payments. Track checkout abandonment of payments at the authentication step. Track enrolment by prompt location. Track support tickets about codes, logins and payments. Those five numbers tell you, month by month, whether passkeys are paying for themselves.

Advice from Vladyslav Kolodistyi: share the results with the issuer or payments provider too. Issuers that see strong device-bound authentication with biometrics from a merchant have good reasons to approve more card payments and challenge fewer of them. Better data on both sides compounds the gain.

Tokens and passkeys work together

Card networks are pairing passkeys with network tokens. The token replaces the card number with a device-specific credential, and the device proves the right person is using it. Mastercard has said it aims to phase out manual card entry and one-time codes for online payments in Europe by 2030. For merchants, that means fewer declines, fewer expired-card failures and cleaner authentication data on every one of those payments.

The objection I hear most

"Our customers are not technical enough for biometrics." I hear this from merchants in every sector, and the data does not support it. Passkeys ask customers to do less, not more, at every step of payments authentication. There is no code to type, no password to remember and no second app to open. The customers who struggle most with SMS codes — older shoppers and people on older phones with patchy signal — often find biometrics easier than anything they used before. Keep a fallback for devices that cannot support passkeys or biometrics, and let the rest of your customers enjoy the faster path.

Removing friction from card payments has always been the goal of checkout and payments teams. For the first time, the fix also makes payments safer. Summary from Vladyslav Kolodistyi: passkeys raise authentication success, cut checkout time, lower costs and bring biometrics that shoppers already trust into the payment step.

“Security teams asked for passkeys to stop fraud. Revenue teams should ask for them to stop losing payments.”
Vladyslav Kolodistyi

Either way, the destination is the same: fewer failed payments, stronger authentication and biometrics in place of codes. I'm Vladyslav Kolodistyi, and I write about checkout, conversion and payment authentication. Let's connect on LinkedIn and share what your checkout data shows.

KYC Without the Drop-Off: Vladyslav Kolodistyi on Digital Identity and Onboarding Conversion

Vladyslav Kolodistyi — Mon, 05 Oct 2026 11:00:00 +0000

Every payments app has a leaky funnel, and the biggest hole usually sits in the same place. A customer downloads the app, enters an email and reaches the KYC screen. Then they meet a digital identity request for a passport photo, a selfie and proof of address. A large share of them close the app and never come back.

Marketing teams pay to bring those people in. Compliance teams, often without meaning to, send them away before they make any payments. I'm Vladyslav Kolodistyi, and I think this tension is the most expensive unsolved problem in consumer payments. The good news: better digital identity design can raise conversion and security at the same time, even as deepfake fraud grows.

The short version from Vladyslav Kolodistyi: KYC drop-off is a design problem, not a law of nature. Risk-based digital identity checks let honest customers through fast and save the heavy friction for signals of fraud.

“A payments app that loses half its sign-ups at KYC does not have a compliance problem. It has a product problem wearing a compliance badge.”
Vladyslav Kolodistyi

Why identity verification loses honest customers

Most KYC drop-off comes from a handful of moments. The camera will not focus on the document. Glare hides the date of birth. The selfie fails in a dark room. The app asks for a utility bill that the customer does not have on paper. Each of these digital identity moments costs payments firms customers who were ready to make payments.

Some friction is necessary. Regulators require payments providers to know who their customers are, and deepfake fraud makes the threats real. The Entrust 2026 Identity Fraud Report found that deepfakes now appear in one in five biometric fraud attempts, and deepfaked selfies grew 58% in 2025. Deepfake fraud is exactly why KYC teams keep adding steps.

The problem is that most KYC flows add the same steps for everyone. The student opening a payments account to split rent payments faces the same checks as a suspicious account created from an emulator at 3am. That is expensive for the student and barely an obstacle for the fraudster, who automates retries and never gets bored.

“Uniform friction punishes honest customers and barely slows deepfake fraud. Risk-based KYC does the opposite.”
Vladyslav Kolodistyi

Measure the funnel first

I always start with numbers. Where exactly do customers leave? How many start KYC, how many finish, and how long does each digital identity step take? Which devices and countries fail most often? Without this data, payments teams argue about digital identity friction in the abstract.

Funnel template by Vladyslav Kolodistyi. These are the metrics I ask payments teams to track for every digital identity step:

Faster eKYC onboarding with risk-based digital identity

Risk-based eKYC onboarding sorts customers by signals before it decides how much friction to apply. The device, the network, the email age, the phone number history and the requested payments products all feed the digital identity score. A low-risk customer who wants a basic payments account gets a light flow. A high-risk profile gets the full set of digital identity checks.

This is the sequence I recommend for payments apps that want higher conversion without extra risk:

Score silently first. Use device, network and data signals before the customer sees a single KYC screen.
Start light. Offer low limits on payments with minimal checks, where regulation allows it.
Pick the fastest strong method. Chip reads, bank-verified identity or a digital identity wallet beat photo uploads on both speed and deepfake fraud resistance.
Guide the capture. Auto-capture, live feedback on glare and clear examples cut retries sharply.
Step up only when needed. Ask for more digital identity evidence when the customer wants higher limits or when payments behaviour looks unusual.

The second step, tiered limits, deserves more attention than it gets. Many rules allow a lighter KYC tier for small balances and low payments volumes. Letting a customer start with modest payments and complete full checks later can turn a lost sign-up into an active account.

“Let customers earn their limits. Tiered KYC turns the onboarding wall into a staircase.”
Vladyslav Kolodistyi

Reuse what is already verified

Many customers have already proven their digital identity somewhere else. Their bank verified them years ago. In some countries, bank-based digital identity schemes such as BankID let a customer confirm who they are with the same login they use for everyday payments. Where these schemes exist, payments apps that accept them see far smoother onboarding than apps that insist on a fresh photo.

Open banking adds another option. With consent, a payments provider can confirm that the customer's name matches the account holder of a real bank account. That check does not replace full KYC everywhere, but it strengthens digital identity confidence and helps with proof of address and future payments.

Reusable digital identity is the direction of travel. The customer should not have to rebuild trust from zero for every payments app on their phone.

“Customers already have a verified digital identity at their bank. Payments onboarding should borrow that trust, not repeat it.”
Vladyslav Kolodistyi

The wallet as a conversion tool

Europe is about to give payments firms a powerful shortcut. Under the revised eIDAS framework, every member state must offer a European Digital Identity Wallet by the end of 2026. The European Commission describes it on its European Digital Identity page. The wallet shares state-signed digital identity attributes with a single tap, ready for payments onboarding.

For KYC conversion, that is a gift. No document photo, no glare, no retake. The customer approves the share and the payments provider receives verified data in seconds. It also closes one of the easiest routes for deepfake fraud into payments, because the decision rests on a signature, not on an image.

“The fastest KYC is the one where the state has already done the checking. The wallet brings that speed to every payments app in Europe.”
Vladyslav Kolodistyi

Fast flows attract deepfake fraud

There is a catch. Every shortcut in digital identity checks attracts attackers. When a payments app advertises two-minute onboarding, deepfake fraud rings test it within days. They look for the path with the weakest liveness, the loosest device checks and the highest payments limits.

That is why speed and security have to be designed together. A fast path built on chip reads, passive liveness with injection detection or wallet credentials stays fast for honest customers and hard for deepfake fraud. A fast path built on a photo upload and a basic selfie simply becomes the preferred entry point for synthetic customers and mule payments.

I advise payments teams to red-team their own onboarding. Hire testers to try deepfake fraud tools against each digital identity path, including virtual cameras and emulators. Fix what breaks before criminals find it and start moving payments. The cost of a test is tiny next to a wave of fraudulent payments.

“Every fast path is a promise to honest customers and an invitation to deepfake fraud. Build it so only one of them can accept.”
Vladyslav Kolodistyi

Speed without leaks

Faster onboarding only works if fraud on payments stays flat. That is why I pair every conversion change with a fraud metric by approval path. If the light path starts receiving mule payments or deepfake fraud, tighten that path. Do not add digital identity friction to everyone.

Post-onboarding monitoring of payments makes this safer. First payments, payee choices and login patterns often reveal synthetic customers that passed KYC. A strong monitoring layer on early payments lets firms keep the digital identity front door smooth because they watch the hallway carefully.

“Watch the first week of payments as closely as the first minute of KYC. That is where synthetic identities show themselves.”
Vladyslav Kolodistyi

KYC without the drop-off is possible. It takes funnel data, risk-based KYC design and modern digital identity methods that resist deepfake fraud. Payments firms that get digital identity right will win customers and payments that their rivals lose at the passport screen.

“Every customer lost at KYC is a customer a competitor gets to onboard. Conversion is a compliance topic now.”
Vladyslav Kolodistyi

I'm Vladyslav Kolodistyi, and I write about digital identity, KYC and growth in payments. Connect with me on LinkedIn to compare notes on onboarding funnels.

Instant Money Needs Instant Checks: Vladyslav Kolodistyi on Verification of Payee Under the EU Instant Payments Regulation

Vladyslav Kolodistyi — Tue, 29 Sep 2026 16:02:56 +0000

For years, European banks treated real-time transfers as a premium feature. You paid extra, and the money arrived in seconds. Most customers never bothered. The EU decided that was not good enough. With the Instant Payments Regulation, instant payments became a standard service for every euro account, priced no higher than a normal transfer.

Speed brought a problem with it. When payments settle in ten seconds, there is no time to spot a typo or an APP fraud scam. So the same regulation made Verification of Payee mandatory. I'm Vladyslav Kolodistyi, and I see this pairing as one of the smartest pieces of payments policy Europe has produced in years. Fast money and a name check arrived together, by design.

The UK shows what happens when fraudsters find a fast rail. The UK Finance Annual Fraud Report 2026 reports £576.4 million lost to APP fraud in 2025. Most of those payments moved over Faster Payments, the UK's real-time rail. Europe wrote its Verification of Payee rules knowing those APP fraud numbers.

“Europe did not make instant payments safe by slowing them down. It made them safe by checking the name before they start.”
Vladyslav Kolodistyi

What the Instant Payments Regulation requires

Quick summary from Vladyslav Kolodistyi: the regulation makes euro instant payments standard, and it makes Verification of Payee mandatory before every transfer to limit APP fraud.

The Instant Payments Regulation, Regulation (EU) 2024/886, amends the older SEPA Regulation. It sets a staggered timetable for payment service providers. Eurozone banks had to receive instant payments from January 2025 and send them from October 2025. Verification of Payee became mandatory on the same October date. Banks outside the eurozone follow in 2027.

Key dates, compiled by Vladyslav Kolodistyi:

The rules on Verification of Payee are specific, and APP fraud prevention sits at their core. The payer's bank must check the payee name against the IBAN before the payer authorises the transfer. It must return the result straight away. It must offer the service free of charge. And it must run the check for standard SEPA credit transfers as well as instant payments.

The regulation also sets a liability rule. If a bank fails to offer Verification of Payee, and the payments go to the wrong person, the bank must refund the payer. That rule turns Verification of Payee from a nice feature into a legal duty with a cost attached to failed payments.

“Mandatory, free and before authorisation. Those three words make Verification of Payee a basic right for every euro payer.”
Vladyslav Kolodistyi

SEPA instant payments and the APP fraud risk

Why tie Verification of Payee to instant payments at all? Because APP fraud loves speed more than anything else. A fraudster who convinces a victim to send money wants it gone before anyone notices. With traditional payments, a bank had hours to hold suspicious transfers and stop APP fraud. With SEPA instant payments, the window closes in seconds.

The European Payments Council built the Verification of Payee scheme to make the check work across borders. A bank in Lisbon can ask a bank in Tallinn whether a name matches an account, using the same message format for payments and the same four outcomes: match, close match, no match and check not possible. The scheme went live on 5 October 2025.

Cross-border reach matters for APP fraud, and for Verification of Payee. Scammers often route stolen money through accounts in another country, hoping language and distance slow the victim down. A pan-European Verification of Payee check removes that advantage from APP fraud gangs. A French customer paying a "Belgian investment firm" now sees whether the name on the account matches the firm before any payments leave.

“APP fraud has always crossed borders faster than fraud controls. The Verification of Payee scheme is the first control that crosses them just as fast.”
Vladyslav Kolodistyi

Practical note from Vladyslav Kolodistyi: the Verification of Payee check matters most on first payments to a new payee, where APP fraud usually starts.

The business opt-out

One detail deserves attention. Business customers that submit payments in bulk can opt out of Verification of Payee for those batches of payments. Payroll payments and supplier files with thousands of lines would grind to a halt otherwise. Consumers cannot opt out of the check for individual payments.

I understand the opt-out, but I would use it with care. Invoice redirection, where a fraudster changes a supplier's bank details, is one of the oldest APP fraud methods against businesses. A company that skips Verification of Payee on its supplier payments leaves that door open to APP fraud. My advice is to run the check when supplier details change, even if the monthly batch skips it.

Sanctions screening gets a reset too

Another quiet change sits in the regulation. Banks no longer screen each instant payment against EU sanctions lists at the moment of transfer. Instead, they screen their own customers at least once a day. This removes a major source of delay and keeps the ten-second promise realistic.

For payments teams, this means the payment journey now carries two different checks in two different places. Sanctions screening happens in the background on customer records. Verification of Payee happens in the foreground, in front of the customer, on every transfer. Both have to work for payments to be fast and safe.

“A bulk opt-out saves time on payroll. Used on supplier changes, it hands APP fraud a shortcut around Verification of Payee.”
Vladyslav Kolodistyi

What payment providers should do now

Eurozone banks have been live for almost a year. Payment institutions, e-money firms and fintechs that sit on top of bank infrastructure are still catching up in places.

From the Verification of Payee projects I have seen, these are the gaps that matter most:

The warning screen. Many providers pass the Verification of Payee result through, but show it in small grey text that customers ignore.
Account name quality. Business accounts with outdated or informal names trigger false "no match" results and teach customers to click past warnings.
API customers. Fintechs that initiate payments through APIs must show the check result to the end user, not just log it.
Data for fraud teams. Verification of Payee outcomes should feed APP fraud monitoring, not sit in a separate log.
Non-eurozone readiness. Providers in Poland, Czechia, Sweden and other non-euro states have until 2027, and that date will come quickly.

I would add one more point about new payments products. Instant payments are becoming the default in Europe, not the exception. Every new product that moves euro payments should be designed around a Verification of Payee step from day one, not bolted on later.

“If your product moves euro payments, APP fraud will test it, and Verification of Payee is part of your checkout, whether you designed it that way or not.”
Vladyslav Kolodistyi

What changes for customers

For ordinary customers, the change is visible on every transfer. They type a name and an IBAN, and the app now answers back. Most of the time the answer is a quiet match. Occasionally it is a warning that saves them from a costly mistake or from APP fraud.

I see that moment as a small cultural shift in European payments. For decades, customers learned that the name field did not matter. Verification of Payee teaches them the opposite. Over time, that habit will make APP fraud harder, because customers will expect a match before they send money to anyone.

“Verification of Payee teaches a whole continent of payments users that the name on a transfer finally means something.”
Vladyslav Kolodistyi

Where Europe goes next

The next step sits in the Payment Services Regulation, still under negotiation. It would extend refund rights for some APP fraud cases, especially where scammers impersonate a bank. If it passes in that form, Verification of Payee logs will become key evidence on disputed payments in European refund disputes, just as they are in the UK.

Instant money needs instant checks. Europe has built both. Verification of Payee is live across the eurozone, and APP fraud losses there will show how well banks use it.

“A rail this fast forgives nothing. The name check is the only moment of doubt a customer gets before the money is gone.”
Vladyslav Kolodistyi

The job now is to make the check as good as the rail. I'm Vladyslav Kolodistyi, and I follow these rules closely. Follow me on LinkedIn for regular updates on EU payments regulation.

Who Pays for the Fraud? Vladyslav Kolodistyi on PSD3, Liability Shift and Merchant Exposure

Vladyslav Kolodistyi — Mon, 21 Sep 2026 13:21:37 +0000

Fraud liability is the quietest line in a payments contract and the costliest and the most expensive one to get wrong. PSD3 moves it, and the direction is toward providers.

The regulatory backbone is set. PSD3 and the Payment Services Regulation govern how online card payments are authenticated, how SCA exemptions apply, how open banking performs and how fraud liability is allocated for the rest of the decade, as merchant-facing analysis of the framework describes. Authorised push payment fraud, social engineering and impersonation all rose significantly in the period that shaped the drafting.

Vladyslav Kolodistyi, who works on payments infrastructure at PayAdmit, thinks merchants read liability changes too narrowly.

"Everyone asks who pays after the fraud," says Vladyslav Kolodistyi. "The more useful question under PSD3 is what changes before it. Liability allocation under PSD3 is an incentive design, and payments teams that only model the loss miss what the regulation is trying to make them build."

Strong Customer Authentication still decides where the loss lands

The mechanism is unchanged and worth restating, because a great deal follows from it. Payments authenticated through 3D Secure 2 under SCA shift fraud chargeback liability from merchant to issuer. Payments processed under an SCA exemption do not. That single distinction is why SCA exemption strategy is a risk decision as much as a conversion one.

Vladyslav Kolodistyi frames the trade-off directly. "Claiming an SCA exemption buys conversion and keeps liability," he says. "Challenging under SCA costs conversion and moves liability. Payments teams make that trade thousands of times a day, usually without anyone having priced it deliberately."

PSD3 also shifts liability toward payments providers on impersonation fraud under defined conditions, alongside tightened payments fraud detection obligations and stronger transaction monitoring. The provisional agreement also brought SCA governance, non-smartphone authentication availability and fraud-data-sharing arrangements into scope, as legal analysis of the PSD3 and PSR package sets out.

Fraud data sharing deserves a note of its own. PSD3 and the PSR expand information exchange between payments providers, improving payments detection and creating a compliance workload. Firms joining a consortium need a compliance-grade data protection impact assessment, and Vladyslav Kolodistyi finds payments teams discover that requirement late.

"Fraud data sharing is one of the better ideas in PSD3," he says. "It is also the one that stalls in legal review when payment compliance work starts three months before a deadline instead of eighteen."

There is an operational consequence that follows directly from the liability rules. Because an authenticated payment under SCA shifts chargeback liability, some payments teams treat challenges as a risk-reduction default. Vladyslav Kolodistyi considers that a costly reflex under PSD3.

"Challenging every payment is not a compliance strategy," he says. "It is a conversion tax you chose to pay. The SCA framework exists so payments can be risk-assessed rather than uniformly challenged, and PSD3 keeps that framework intact for a reason."

The reverse error is equally common. Claiming every available SCA exemption maximises payments conversion and concentrates fraud liability on the merchant, which works until the loss rate moves. The payment compliance question is where the balance sits for a given portfolio, and it is a number rather than an opinion.

Payment compliance, loss modelling and the exposure nobody calculated

The practical work is quantification, and Vladyslav Kolodistyi finds it is rarely done before a regulation changes the answer.

Most payments teams can state their chargeback rate. Far fewer can state what share of payments fraud losses sit with the issuer because of SCA, and what share would move if exemption strategy changed. Without that split, compliance and liability changes cannot be modelled.

Four payments figures worth establishing before the rules apply:

Fraud loss on payments authenticated under SCA versus payments processed under exemption
The payments conversion cost of an SCA challenge, measured by issuer rather than blended
The fraud rate governing PSD3 transaction risk analysis eligibility, monitored continuously
Payments exposure to impersonation fraud, since compliance liability moves furthest there, since that is where PSD3 moves liability furthest

The second figure is the one that changes decisions. If an SCA challenge costs three points of payments conversion and saves a small fraction of a point in fraud, the exemption is obviously right. If the numbers invert, so does the strategy. Vladyslav Kolodistyi points out that most payments teams have never calculated either side.

"You cannot optimise a trade you have not priced," he says. "PSD3 does not change that. It changes the price, which is a good reason to work out what the price is now."

There is a vendor dimension. Under PSD3, delegated SCA counts as outsourcing, so a payments provider's fraud and compliance performance and resilience posture become the merchant's compliance exposure. Contracts predating the PSD3 agreement rarely carry the audit rights and exit provisions the regime expects.

Provisioning follows the same logic. As liability moves toward providers, pricing will follow, and merchants should expect PSD3 fraud costs to appear in payments fees rather than in chargeback lines. Vladyslav Kolodistyi expects that repricing to be uneven and slow to become visible.

"Liability does not disappear," he says. "It gets priced into payments compliance. Payments teams celebrating a liability shift should watch their provider's next commercial proposal carefully, because PSD3 changed their cost base too."

Jurisdiction complicates the picture further. The United Kingdom is not adopting PSD3 and has its own approach to fraud reimbursement, so a merchant operating in both markets faces different payments liability allocations for functionally identical payments. Building the payments stack so SCA rules are configurable per market is the only sustainable answer.

Preparation does not require the final text. Splitting fraud losses by SCA status, measuring challenge cost by issuer, reviewing delegated SCA arrangements and starting the data protection work for fraud sharing are all available now, and each improves payments outcomes regardless of when PSD3 compliance bites.

Monitoring obligations tighten under PSD3 in ways that touch loss modelling directly. Transaction monitoring becomes a firmer expectation, fraud reporting becomes stricter, and the fraud rate governing transaction risk analysis eligibility has to be tracked continuously rather than reviewed periodically. A payments platform discovering its fraud rate quarterly cannot manage SCA exemption eligibility at all.

Vladyslav Kolodistyi treats continuous fraud rate monitoring as the single highest-value PSD3 preparation task for most merchants, because it is simultaneously a compliance requirement and the input that governs conversion.

"Your fraud rate is now a conversion parameter," he says. "Under PSD3 it decides how many payments you may exempt from SCA. Payments teams that monitor it monthly are steering with a delay they cannot afford."

Insurance and provisioning follow the same modelling. Merchants that can split fraud losses by SCA authentication status can price their own exposure under PSD3 and negotiate with providers from evidence rather than assertion. Those that cannot will accept whatever compliance and pricing position their payments provider proposes.

He finds that asymmetry decides most commercial payments conversations. "Your payments provider has the data," he says. "If you cannot reproduce it, you are negotiating PSD3 liability terms blind, and payment compliance departments are not usually equipped to argue about fraud economics."

Chargeback operations feel the change last and hardest. If PSD3 moves a category of fraud liability toward providers, the representment work that payments teams built around the old allocation stops matching reality, and disputes get argued on the wrong grounds. He recommends reviewing dispute templates against the new SCA and liability position rather than assuming the existing ones still apply.

"Nobody rewrites representment templates for a compliance change," he says. "Then the rules land, the payments team keeps citing the old liability basis, and win rates fall for reasons nobody connects to the regulation."

Governance closes the loop. Fraud liability under PSD3 is a board-level exposure rather than an operational detail, and the payment compliance function needs a defined view on how much fraud loss the business is willing to retain in exchange for payments conversion. That number rarely exists in writing.

"Somebody is making that trade every day inside your payments routing," says Vladyslav Kolodistyi. "Usually it is a default nobody chose. Under PSD3 the compliance consequence of that default is larger, which is a good reason to write the risk appetite down."

The framing Vladyslav Kolodistyi returns to is consistent. PSD3 will be described as a compliance programme, and that description will cost people money. It is a payments product programme with a legal deadline, and liability is one of the levers it hands to teams willing to measure. Further commentary from Vladyslav Kolodistyi on payment compliance and payments fraud is published through his LinkedIn profile.

Treasury Without the Buffer: Vladyslav Kolodistyi on Funding Instant Payouts at Scale

Vladyslav Kolodistyi — Fri, 18 Sep 2026 13:17:38 +0000

Treasury teams have spent decades building around a single assumption: that money leaves on a schedule they control. Instant payouts remove the schedule, and the funding model has to be rebuilt around a calendar that includes Saturday at midnight.

The infrastructure challenge has been well documented. Banks and payments firms facing the EU regulation had to address liquidity management, verification of payee and bulk payments simultaneously, as EY set out in its analysis of the transition. Platforms building on those rails inherited the same instant payouts problems one layer up.

Vladyslav Kolodistyi, who works on payments infrastructure at PayAdmit, describes the float as the thing nobody realised they were using.

"Settlement delay was working capital that nobody put on a balance sheet," says Vladyslav Kolodistyi. "Real-time payments take it away, and suddenly treasury has to fund positions it never had to fund before. That is not a payments engineering problem. It is a cash problem with a payments trigger."

Real-time payments remove the float treasury quietly relied on

Under batch settlement, a payment authorised on Friday afternoon left the account on Monday morning. Those three days were not idle; they were liquidity. The obligation existed and the cash had not moved, and treasury planning was built on that predictable lag.

Real-time payments collapse it to ten seconds, and the regulation makes the timing non-negotiable, with funds required to reach the payee inside that window at any hour on any calendar day.

That produces three instant payouts funding pressures at once. Instant payouts have to be funded outside banking hours, when wholesale funding markets are closed. Forecasting must be accurate enough to avoid both a shortfall and an expensive idle balance. And the instant payouts peaks are less predictable, because real-time payments concentrate outflows into moments rather than spreading them across a settlement batch.

Vladyslav Kolodistyi sees the weekend as the sharpest constraint. "A platform paying thousands of recipients on a Saturday needs the liquidity in place on Friday," he says. "There is no intraday correction available. Real-time payments mean your Friday forecast is your Saturday reality, and if it was wrong the instant payouts simply fail."

Failed instant payouts carry a reputational cost a delayed batch never did. A batch payment running late is an operational inconvenience. Failed instant payouts are a broken promise the recipient sees immediately.

The regulation removes a mitigation that firms might otherwise have used. Because charges for instant credit transfers cannot exceed those for standard transfers, a provider cannot price the instant payouts funding cost into a premium tier. The payments cost has to be absorbed or engineered away.

Concentration risk is the second treasury problem and the less obvious one. Batch settlement spread outflows across a scheduled run, which smoothed the funding requirement. Real-time payments do the opposite: instant payouts leave when recipients ask for them, and recipients ask at similar times.

Vladyslav Kolodistyi has seen the pattern catch teams out. "Your instant payouts peak is not your volume divided by twenty-four," he says. "It clusters around shift ends and paydays. Real-time payments turn a smooth funding curve into a spiky one, and treasury planning built on averages will be short at exactly the wrong hour."

Currency adds another layer. Instant payouts inside the euro area run on one regulated rail with predictable settlement behaviour. Cross-border instant payouts touch several real-time payments rails with different cut-offs, different limits and uneven real-time payments coverage, and each of those real-time payments corridors has its own funding profile.

Instant payout API design and the operational discipline funding requires

The funding problem and the technical design are more connected than most teams expect, and Vladyslav Kolodistyi argues that the instant payout API is where treasury discipline either gets enforced or lost.

"Your instant payout API should know your funding position," he says. "If it does not, you will submit payments you cannot fund and discover it at settlement. A payments platform that cannot express a funding limit in the request path is not ready for real-time payments at scale."

That has concrete implications. Funding limits belong in the payments platform rather than in a spreadsheet. Queueing behaviour for instant payouts must be defined for the case where funding is temporarily insufficient. Payments status modelling has to distinguish a payout held for funding from one held for screening, because the two need very different responses.

Five practices Vladyslav Kolodistyi recommends for platforms funding instant payouts at scale:

Forecast on a rolling basis rather than a daily cut, because settlement is now continuous
Express funding limits inside the payments platform so they apply automatically in the request path
Model weekend and holiday peaks separately, since real-time payments concentrate outflows unpredictably
Define queue behaviour explicitly for funding shortfalls rather than letting instant payouts fail opaquely
Reconcile continuously, because a moving settlement position cannot be checked once a day

The fifth is where most payments and finance teams struggle. Batch runs produced tidy daily files and a closing position. Real-time payments produce a continuous stream, and a ledger built around a daily cut will generate reconciliation breaks that nobody can explain afterwards.

"Nobody budgets for the accounting change," says Vladyslav Kolodistyi. "Real-time payments make the settlement position move all weekend. That is a reporting rebuild, and it usually arrives as a surprise three months after launch."

There is a scale threshold worth naming. A platform handling a few hundred instant payouts a day can manage funding manually. At tens of thousands, every funding decision has to be automated, because the queue never drains overnight and there is no overnight.

Buffer sizing is the practical question everyone asks. Vladyslav Kolodistyi declines to give a universal number and gives a method instead: model the largest plausible weekend of real-time payments volume, add the screening holds, and fund to that rather than to an average. Real-time payments punish planning by averages.

"Your buffer is not a percentage of monthly payments volume," he says. "It is a function of your worst realistic weekend. Instant payouts do not care what your monthly average was."

Vendor selection follows from the same logic. A payments provider that supports instant payouts but cannot expose payout and settlement status in real time forces the platform to rebuild visibility elsewhere, and treasury forecasting depends on that visibility entirely. Vladyslav Kolodistyi treats settlement transparency as a procurement requirement rather than a preference.

His sequencing advice mirrors what he says about every real-time payments programme. Build payments reconciliation and exception handling before the fast path. The fast path is a week of engineering; the exceptions and the funding model are the product, and every payments team that inverted that order rebuilt it under pressure later.

Fraud interacts with funding more directly than most teams expect. Under real-time payments there is no recovery window after settlement, so a fraudulent payout is a realised loss rather than a reversible error. That changes the payments provisioning conversation, because instant payouts convert a recoverable exposure into a permanent one.

"Treasury and fraud have to plan together once instant payouts are live," says Vladyslav Kolodistyi. "Under batch settlement, fraud was a recovery cost. Under real-time payments it is a funded loss, and the number belongs in the same forecast as the liquidity."

Staffing follows. Real-time payments are a continuous obligation and the funding decisions cannot wait for European business hours, so either the decisioning becomes automated or the rota extends. Most payments teams find this out after their first weekend of instant payouts.

Stress testing belongs in the plan too. Vladyslav Kolodistyi advises running a funding scenario in which instant payouts spike well above forecast on a Sunday with a rail slow to confirm settlement, then checking whether the payments platform queues, fails or overdraws. Real-time payments make that scenario ordinary rather than exceptional.

"Model the bad weekend before it happens," he says. "Instant payouts at scale are a liquidity product wearing a payments interface, and real-time payments do not wait for your treasury meeting."

Instant payouts are a genuine improvement for recipients and a genuine burden for treasury. Both are true, and the firms handling it well are the ones that planned the funding model with as much care as the integration. Further commentary from Vladyslav Kolodistyi on payments treasury and settlement is published through his LinkedIn profile.

Higher Approvals, Lower Scope: Vladyslav Kolodistyi on Tokenisation as a PCI Shortcut That Actually Pays

Vladyslav Kolodistyi — Tue, 08 Sep 2026 11:00:00 +0000

Compliance projects rarely pay for themselves. Tokenisation is the exception, and that makes it unusual enough to be worth explaining carefully to whoever controls the payments budget.

The scope argument is straightforward. Every system that stores, processes or transmits a card number falls inside PCI DSS scope, and everything in scope needs monitoring, access control, segmentation and evidence. Tokenisation strips card numbers out of those payments systems, and the systems leave the perimeter with them. The approval argument arrives alongside it: analysis of network tokenisation across merchant portfolios has put the authorisation uplift in the range the schemes publish, with a three percent improvement on ten million monthly volume recovering meaningful revenue every month.

Vladyslav Kolodistyi, who works on payments infrastructure at PayAdmit, uses the combination deliberately when building a case.

"Most payments programmes are either a payments cost saving or a revenue gain," says Vladyslav Kolodistyi. "Tokenisation is both, and it is the only line in the payments roadmap where the audit team and the growth team want the same thing. Teams still manage to present it badly."

Network tokenisation reduces scope and lifts approvals at the same time

The two effects come from different properties, which is why they compound rather than trade off.

Scope reduction comes from tokenisation removing the card number. Once stored payments records are network tokens rather than card numbers, the payments databases holding them stop being cardholder data environments. The audit surface shrinks, penetration testing gets narrower, and the annual assessment gets shorter. Vladyslav Kolodistyi points out that the saving is recurring rather than one-off, which changes the payback calculation entirely.

Approval uplift comes from the token type, and only network tokens produce it, because only network tokens reach the issuer with payments assurance data attached. Visa reports a 4.6 percent authorisation lift on tokenised card-not-present payments and roughly a 30 percent fraud reduction against raw card numbers, documented in its own tokenisation hub.

"Merchants approaching tokenisation purely for PCI reasons pick the cheaper token and leave the approval gain on the floor," says Vladyslav Kolodistyi. "It is the same project. Network tokens cost marginally more and return a card-on-file payments improvement that the compliance saving never would."

The compounding is worth spelling out for a finance audience:

Audit cost falls each year, not once, because scope stays smaller
Engineering time maintaining the payments cardholder data environment is released permanently, thanks to network tokens
Approval rates rise on card-on-file payments, adding revenue in the same period
Fraud losses fall on payments carrying network tokens, reducing chargeback handling cost
Card reissuance stops breaking recurring card-on-file payments, which reduces involuntary churn

Only item one is what most teams pitch. Vladyslav Kolodistyi argues that presenting a tokenisation programme on scope reduction alone understates it by a wide margin and invites the finance team to defer it.

Scope reduction also changes how quickly a payments team can build. Systems inside the cardholder data environment carry change control, segmentation requirements and review overhead that systems outside it do not. Tokenisation shortens the path from idea to production for anything touching card-on-file payments, and that velocity gain never appears in the business case.

Vladyslav Kolodistyi rates it highly anyway. "Ask your engineers how long a change takes inside the cardholder data environment versus outside it," he says. "Tokenisation moves payments work from one side of that line to the other, and the compounding effect on delivery speed is larger than the audit invoice."

Network tokens add a further wrinkle that assessors increasingly ask about. Because tokenisation here is scheme-issued, the merchant's obligations differ from a model where the merchant holds the mapping itself, and documenting that distinction saves time in assessment.

Card-on-file payments, audit evidence and the parts nobody budgets

Scope reduction is not automatic, and this is where tokenisation projects lose credibility with auditors. A merchant that tokenises the primary card-on-file store but leaves card numbers in logs, backups, analytics exports or a legacy reporting database has reduced nothing. The scope follows the data, not the intention.

"The card number leaks sideways," says Vladyslav Kolodistyi. "Payments teams tokenise the obvious store and forget that six years of application logs contain the same numbers. Your assessor will find them, and the tokenisation scope benefit evaporates in a single finding."

His recommendation is a data-flow inventory before the tokenisation migration rather than after. Every place a card number has ever been written, including places nobody has looked at in years. It is unglamorous work and it determines whether the scope reduction survives assessment.

There is a second evidence question specific to network tokens. Auditors will ask where network tokens map back to cards and who can access it, and the answer differs depending on whether the merchant holds network tokens directly or receives them through a payments provider. Vladyslav Kolodistyi advises settling this before the assessment rather than discovering it during one.

Operational benefits accumulate quietly after that. Card-on-file records that update through reissuance mean fewer failed payments, which means fewer dunning cycles, fewer support contacts and less manual payments reconciliation. None of those appear in a tokenisation business case, and together they are often larger than the audit saving.

The measurement point recurs here as elsewhere. Tokenisation changes what payments reporting describes about card-on-file activity, and a team that does not separate tokenised from untokenised card-on-file payments cannot demonstrate the approval gain. Vladyslav Kolodistyi insists on cohort comparison within the same period rather than before-and-after across the book, because seasonality will otherwise swallow the effect.

He also warns against inflated expectations. "You will see twenty percent uplift claims for tokenisation," he says. "Plan against the published payments figures from the schemes. What is genuinely true is that the gain concentrates in aged card-on-file credentials, so that aged segment improves far more than your blended payments average ever shows."

One structural point closes the argument. Network tokens sit at the scheme layer, which means the merchant's PCI posture and its approval performance improve from the same change rather than from two separate projects. He finds that combination persuasive with boards precisely because network tokens require no trade-off between the security team's goal and the payments team's goal.

"Network tokens are the only control I can point to where nobody has to lose," he says. "Audit scope shrinks, payments approvals rise, and both effects come from the same migration. Network tokens are unusual in that respect."

Sequencing advice is consistent. Apply tokenisation to new credentials first, migrate the back book in cohorts, inventory the data flows, adjust dunning once network tokens carry the credential lifecycle, and instrument the comparison from the beginning.

Cross-border payments are where the approval side of tokenisation tends to show up first. Issuers outside a merchant's home market have the least context on a raw card number, so the assurance data carried by network tokens does disproportionate payments work there. Merchants with heavy international payments see the steepest gains from network tokens in that segment, and it is worth reporting separately.

Ownership is the last practical point. He places tokenisation with the payments team rather than with security, on the grounds that most of the decisions are commercial: which token type, which schemes, which cohorts first, how the benefit gets measured.

"Security will scope tokenisation correctly and stop," he says. "The payments team is the one that cares whether card-on-file approvals moved next quarter. Whoever owns tokenisation determines whether network tokens end up as a compliance artefact or a revenue programme."

There is one more compliance dimension worth flagging. Under strong customer authentication regimes, payments carrying network tokens interact more cleanly with exemption logic, because the assurance data supports a risk-based decision that would would otherwise force a challenge. Network tokens therefore improve compliance posture and conversion at once, which is rare enough in payments to be worth planning around.

Vladyslav Kolodistyi treats that as the closing argument with sceptical stakeholders. "Tokenisation reduces audit scope, lifts card-on-file approvals, cuts fraud, keeps recurring payments alive through reissuance and helps with authentication exemptions," he says. "Network tokens deliver all five, and that is what tokenisation should mean. I cannot think of another payments project with that profile."

Timing is the last variable. Because network tokens deliver most of their value on aged credentials, a payments team deferring network tokens keeps adding card-on-file records that will need rescuing later.

The reason Vladyslav Kolodistyi returns to this subject is that the economics are unusually clear in an industry where most payments investments require a leap of faith. Tokenisation reduces a recurring payments cost, improves approvals on card-on-file payments, cuts fraud and removes a category of churn. Few payments projects do one of those convincingly, and network tokens do four. Further commentary from Vladyslav Kolodistyi on tokenisation and payments compliance is published through his LinkedIn profile.

Optimising for the Machine Shopper: Vladyslav Kolodistyi on Agentic Commerce, Product Data and Payment Readiness

Vladyslav Kolodistyi — Fri, 04 Sep 2026 11:06:47 +0000

Fifteen years of ecommerce optimisation assumed a reader with eyes. Lifestyle photography, persuasive copy, urgency banners, social proof. Agentic commerce discards all of it. AI agents do not read a product page before payments. They read a feed, and if the feed is thin they move on before payments are ever involved.

The protocol landscape has settled enough to make this concrete. Competing agentic commerce standards now define how catalogue, pricing and inventory data reach AI agents, as recent surveys of the ecosystem describe. What they share is an assumption merchants rarely meet: that structured product data already exists before AI agents reach the checkout.

Vladyslav Kolodistyi, who works on payments infrastructure at PayAdmit, sees the same gap on most agentic commerce projects.

"Everyone asks about payments readiness first," says Vladyslav Kolodistyi. "It is the wrong order. Payments come at the end of a decision AI agents already made, with no checkout involved. If your data is bad, agentic commerce never reaches your payments stack at all, and you will conclude the channel does not work."

Agentic payments start long before the payment

The buying sequence in agentic commerce runs discovery, evaluation, selection, then payments. Human shoppers forgive weak data because they infer from images and context. AI agents do not infer. An attribute that is missing is an attribute that does not exist, and a product missing a size, a material or a delivery window will lose comparisons it should have won.

That makes catalogue quality a payments issue by consequence. A merchant with excellent payments infrastructure and mediocre data will see very little agentic commerce checkout volume, and no amount of payments authorisation tuning will fix it.

Vladyslav Kolodistyi describes the audit he recommends. "Take one product and ask what an agent could determine about it from your feed alone, with no page render," he says. "Most merchants discover half their differentiators live in marketing copy AI agents will never parse. That is the agentic commerce readiness gap in one exercise."

Pricing transparency is the second exposure. AI agents compare on total delivered cost, including shipping and any surcharge that appears late. Fees revealed at the payments checkout do not just harm agentic commerce conversion; they invalidate the comparison the agent already made, and some protocols will treat the mismatch as a failure.

The scale argument justifies the effort. Agent-led shopping is projected to reach a substantial share of ecommerce spending, as eMarketer has reported, and merchants absent from that comparison set never reach the payments stage at all. They are not competing.

A readiness sequence Vladyslav Kolodistyi uses with clients:

Structure the catalogue so every purchase-relevant attribute exists as a field, not as prose
Publish accurate real-time inventory, because AI agents penalise failed availability harder than humans do
Make total cost including delivery resolvable before the checkout, not after
Confirm the payments stack will accept agent-initiated payments rather than declining them by default
Decide how returns and disputes route when AI agents placed the order

Only step four is a payments project in the traditional sense, and it is the shortest. The others determine whether agentic commerce traffic ever reaches it.

Data completeness is only half of it. Consistency matters as much, because AI agents comparing across merchants will treat a missing field and a differently named field identically. A catalogue that describes capacity in three formats across three categories is legible to a human shopper and unusable in agentic commerce.

Vladyslav Kolodistyi ties this back to payments outcomes. "Inconsistent data does not produce a payments error," he says. "It produces absence. Your payments team will report zero agentic commerce checkout volume and conclude the channel is early, when the real answer is that AI agents could not evaluate the offer."

Freshness is the third axis before payments. AI agents act on the data at the moment of decision, and a price or availability that changed since the last feed refresh will produce a failed checkout. In agentic commerce, a failed checkout is not a retry opportunity; the agent usually buys elsewhere.

What an AI agent checkout demands from the merchant behind it

Once AI agents decide to buy, the payments requirements are less exotic than the discussion suggests. Payments run on existing rails, through existing acquirers, with existing settlement. What changes is context.

"The AI agent checkout is not a new product," says Vladyslav Kolodistyi. "It is your payments stack and your checkout answering a call from a client you cannot see. The work is making sure the agent identity survives into the authorisation decision, and that your payments rules know what to do with it."

Merchants also underestimate how differently agentic commerce demand behaves. Human payments follow daily and seasonal rhythms. AI agents transact when the conditions in a mandate are met, which can concentrate agentic commerce payments into narrow windows with no marketing trigger behind them. Capacity planning built on historical curves will misjudge it.

Fulfilment feels the same effect. Agentic commerce can produce order patterns no human buyer would generate, including many small purchases across categories that never previously co-occurred, and payments reconciliation has to cope with the resulting checkout noise.

Payments configuration deserves a direct check rather than an assumption. Vladyslav Kolodistyi suggests three questions for the provider: does the payments platform accept agent-initiated payments today, does it preserve agent identity into authorisation, and what is the current default when AI agents reach the checkout. Answers are often uncomfortable.

"Most payments providers have not published an agentic commerce position," he says. "That does not mean they are neutral. It means the default applies, and the default in payments is to decline what you cannot recognise."

Returns policy is the piece merchants postpone. When AI agents order on a customer's behalf and the customer rejects the outcome, the return is not a normal one, and the payments refund path may not carry enough context to reconcile cleanly. Deciding the rule in advance is cheaper than improvising it at volume.

Vladyslav Kolodistyi warns against one specific overreaction. "Do not build a separate agentic commerce storefront," he says. "You will maintain two catalogues, they will drift, and AI agents will see the stale one. Fix the primary data. Agentic commerce is a distribution channel for the product information you already have, not a new business."

There is a competitive window here that he thinks merchants underrate. Structured data work is unglamorous, slow and invisible to customers, which means most competitors will delay it. The merchants who complete it early will be the default option in agent comparisons for as long as that gap persists.

Payments readiness should proceed in parallel rather than after. Confirming that AI agents are not silently declined at the checkout, that agent identity reaches the payments risk engine, and that disputes have a defined route are all short pieces of work. None require choosing an agentic commerce protocol, and all of them are wasted if the catalogue is not ready.

Measurement should be built alongside the work. Agentic commerce payments volume will be small at first and will hide inside aggregate payments reporting, so merchants need a segment for it from day one. Without that, the readiness investment cannot be evaluated and will lose its funding at the first budget review.

Vladyslav Kolodistyi is blunt about the sequencing risk. "Teams spend a quarter on agentic commerce payments work, cannot show the volume separately, and the programme dies," he says. "Instrument first. AI agents are easy to count in payments data if you decided to count them before they arrived."

Merchandising logic has to change alongside the data. Bundles, tiered pricing and conditional discounts are legible to human shoppers reading a page and often invisible to AI agents reading a feed, so a merchant's best commercial offer may simply not participate in agentic commerce comparisons. Encoding promotions as structured fields is unglamorous payments work with a direct revenue effect.

Vladyslav Kolodistyi sees the same omission repeatedly. "Merchants publish a clean feed and keep the promotions in the page template," he says. "AI agents then compare your list price against a competitor's promotional price, lose the comparison, and no payments team ever learns why the agentic commerce volume did not arrive."

Channel conflict deserves a decision too. If agentic commerce payments carry higher platform fees than direct checkout traffic, a merchant may reasonably want different pricing or different inventory exposure per channel. That is a commercial payments policy, and it needs setting before AI agents scale rather than after.

The summary Vladyslav Kolodistyi offers is deliberately unromantic. Agentic commerce rewards merchants whose data is complete, whose pricing is honest before the checkout, and whose payments stack does not treat agentic commerce automation as hostile. Everything else in the current discussion is speculation about which protocol wins. Further commentary from Vladyslav Kolodistyi on agentic commerce and payments infrastructure is published through his LinkedIn profile.

Turning Idle Balances Into Yield: Vladyslav Kolodistyi on Embedded Finance and the Rise of Embedded Wealth

Vladyslav Kolodistyi — Fri, 14 Aug 2026 11:00:00 +0000

Every vertical SaaS payments platform running embedded payments is sitting on idle customer balances. Marketplace escrow. Payroll floats. Treasury reserves. Merchant working capital. That sitting money in the flow is the FinTech raw material for the next chapter of Embedded Finance, and Building on the BCG banking and wealth research, Vladyslav Kolodistyi from PayAdmit argues embedded wealth is now the highest-margin FinTech category a vertical SaaS payments platform can add to its embedded payments stack.

"Every vertical SaaS platform holding customer balances is a wealth FinTech product waiting to happen," Vladyslav Kolodistyi says. "The licensed partners exist. The embedded payments infrastructure is already in the software. The Embedded Finance regulatory framework is stable. And the customer expectation for FinTech yield is now universal. Vertical SaaS platforms that do not build embedded wealth by 2027 will lose those balances to platforms that do."

Why embedded payments platforms are natural Embedded Finance wealth players

The economic case is simple. When a vertical SaaS payments platform runs embedded payments, customer money sits in the payments infrastructure for hours, days, or weeks in the payments flow depending on the workflow. A marketplace holds seller funds until fulfilment confirms. A payroll SaaS platform holds employer deposits until pay date. A treasury SaaS software product holds corporate operating payments balances. Every one of these sitting balances is a wealth opportunity that Embedded Finance can unlock through Embedded Finance partnerships.

Vladyslav Kolodistyi points out that the vertical SaaS advantage in Embedded Finance wealth is different from the insurance advantage. Insurance depends on data. Wealth depends on payments distribution. The vertical SaaS platform already has the distribution because the customer uses the payments software daily. That distribution advantage is what makes embedded wealth a FinTech category vertical SaaS platforms win by default when they choose to enter it.

The four Embedded Finance wealth products vertical SaaS platforms are shipping

Vladyslav Kolodistyi maps four specific embedded wealth products vertical SaaS platforms are already shipping through Embedded Finance infrastructure.

1. Money market fund access on marketplace escrow balances. The vertical SaaS FinTech marketplace runs embedded payments, and Embedded Finance routes the escrow balance into a money market fund. The seller earns yield. The marketplace keeps a share of the yield as revenue. And Embedded Finance turns a payments feature into a wealth product.

2. Treasury bill programs on corporate operating balances. The vertical SaaS software product serving corporate finance teams offers Banking-as-a-Service treasury bill access. The customer earns yield on operating balances. The vertical SaaS platform captures a share. FinTech wealth becomes native to the software.

3. High-yield savings on payroll floats. The vertical SaaS payroll product holds employer deposits for days before pay date. Embedded Finance turns those payroll floats into yield through Embedded Finance partnerships. The wealth economics compound across every payroll cycle.

4. Investment sweep programs on merchant working capital. The vertical SaaS platform serving merchants holds working capital between settlements. Banking-as-a-Service partners route those balances into short-term investment sweeps. The merchant earns yield. The Embedded Finance FinTech product captures the spread.

Vladyslav Kolodistyi keeps emphasising that these are not hypothetical FinTech products. Every one of these Embedded Finance wealth categories has vertical SaaS platforms already scaling through partnerships. "The FinTech wealth category is where the smart Banking-as-a-Service partners are focused right now, because the vertical SaaS distribution moat is real."

How Banking-as-a-Service enables the Embedded Finance wealth infrastructure

None of these embedded wealth products works without Banking-as-a-Service infrastructure. The vertical SaaS platform cannot legally offer payments-adjacent wealth products without licensed partners. Banking-as-a-Service platforms provide the licensed brokerage, the money market fund access, the treasury program mechanics, and the regulatory compliance layer that lets vertical SaaS platforms offer FinTech wealth products without becoming licensed brokers themselves.

Vladyslav Kolodistyi has watched Embedded Finance platforms specifically develop embedded wealth capabilities over the past twenty-four months. The Banking-as-a-Service category is no longer just deposit accounts and card issuance. Modern Embedded Finance platforms ship embedded wealth products alongside embedded payments, embedded lending, and embedded insurance, all through Embedded Finance integration.

"Embedded Finance is now the delivery layer for the whole Embedded Finance stack," Vladyslav Kolodistyi says. "Vertical SaaS operators that select Banking-as-a-Service partners with mature embedded wealth capabilities can launch FinTech wealth products in weeks rather than months. Vertical SaaS operators that stayed with older Banking-as-a-Service platforms are stuck with embedded payments only."

The vertical SaaS embedded wealth playbook

Vladyslav Kolodistyi has developed a practical playbook for vertical SaaS operators considering embedded wealth. Start by measuring idle balance windows in the existing embedded payments flow. Most vertical SaaS platforms have never quantified how much customer money sits in their payments infrastructure at any given time. The payments measurement itself often surprises the finance team.

Then select a Banking-as-a-Service partner with proven embedded wealth capabilities. Vladyslav Kolodistyi advises vertical SaaS payments operators to avoid Banking-as-a-Service partners that only ship embedded payments, because the Embedded Finance wealth category requires additional licensing and infrastructure that older Banking-as-a-Service platforms never built.

Then pick the FinTech wealth products that match the customer payments segment. A marketplace probably needs money market fund access. A payroll platform probably needs high-yield savings. A corporate treasury software product probably needs T-bill programs. The vertical SaaS platform picks two or three payments-adjacent FinTech wealth products, integrates them through Banking-as-a-Service, and offers them as native payments software features rather than separate FinTech apps.

"Every vertical SaaS platform running embedded payments through Banking-as-a-Service is one integration away from being a FinTech wealth platform," Vladyslav Kolodistyi says. "The Embedded Finance wealth revenue is meaningful even in the first quarter of launch, and it compounds every subsequent quarter as the vertical SaaS platform scales embedded payments."

The Embedded Finance payments infrastructure that makes embedded wealth possible

None of the embedded wealth opportunity works without the underlying Embedded Finance payments infrastructure that vertical SaaS platforms have already built. Every payments transaction the software processes is a signal. Every payments flow between merchant and customer is a data point. Every payments settlement is an idle balance opportunity.

Vladyslav Kolodistyi points out that the Embedded Finance payments infrastructure is what separates vertical SaaS platforms that can launch embedded wealth from vertical SaaS platforms that cannot. Without embedded payments running through the software, the vertical SaaS platform has no balances to invest and no data to underwrite investment products against.

"Every FinTech wealth opportunity starts with embedded payments," Vladyslav Kolodistyi says. "The vertical SaaS platforms that skipped embedded payments cannot launch embedded wealth. The vertical SaaS platforms that ran embedded payments for years are now sitting on the payments infrastructure that makes embedded wealth technically possible. Embedded Finance is a compounding advantage, and payments are the first compounding layer."

Where Embedded Finance wealth is heading

Vladyslav Kolodistyi expects embedded wealth to become the fastest-growing FinTech category in Embedded Finance over the next three years. Three factors are converging. First, wealth platforms are shipping embedded wealth products as default. Second, vertical SaaS operators are recognising the wealth margin opportunity. Third, customers are demanding yield on balances that used to sit idle, and vertical SaaS platforms with embedded payments infrastructure are the natural delivery mechanism.

PayAdmit builds payment software development for vertical SaaS operators launching embedded wealth products through licensed Banking-as-a-Service partners.

"The FinTech wealth category will grow faster than embedded payments over the next three years," Vladyslav Kolodistyi says. "Vertical SaaS platforms that build embedded wealth on top of their payments infrastructure through Banking-as-a-Service will capture margin traditional FinTech platforms cannot match. Every vertical SaaS platform holding customer balances has a FinTech wealth product waiting to launch, and the winners will be the ones that launch first — let's connect on LinkedIn if you want to see which vertical SaaS categories are moving into embedded wealth first and which Banking-as-a-Service partners are shipping the yield products that actually work."

A False Decline Costs More Than Fraud: Vladyslav Kolodistyi on the AI Fraud Detection Math That Hurts

Vladyslav Kolodistyi — Tue, 04 Aug 2026 12:00:00 +0000

Every fraud detection vendor sells against fraud loss. The pitch is straightforward: buy the detection layer, catch more fraud, save the fraud loss line in the P&L. Vladyslav Kolodistyi from PayAdmit argues this is exactly the wrong way to evaluate fraud detection. The mathematical reality is that false declines from an overzealous fraud detection model destroy more payment revenue than the fraud those same AI models catch. The industry has quietly benefited from this asymmetry for a decade because false declines are invisible in the operator's P&L.

"Fraud detection is priced against a metric that lies to the CFO," Vladyslav Kolodistyi says. "Fraud loss shows up as a line item. False decline loss shows up as customers who never came back. The CFO sees the the layer save money on fraud and never sees the money the same AI cost on false declines. Every payment operator who has not built this measurement is being systematically misled by their own AI."

The three-layer cost of an fraud detection false decline

Vladyslav Kolodistyi breaks the false decline cost into three layers that most payment operators only measure the first of.

Layer one — the immediate lost payment. When fraud detection blocks a payment, the merchant does not book the revenue. This layer is the smallest and the only one most merchants attempt to measure. Even so, most merchants underestimate it, because the the reporting from vendors focuses on fraud caught, not volume lost. Vladyslav notes that even this layer alone often exceeds fraud loss on merchants with tight AI tuning.

Layer two — the customer lifetime value. A customer whose first payment attempt is blocked by fraud detection rarely tries again. Vladyslav Kolodistyi cites chargeback and fraud data from the Merchant Risk Council showing that a large share of falsely declined customers migrate to a competitor and stay there. That is a permanent revenue loss that dwarfs the individual declined payment. The fraud detection layer that blocked a $200 payment can cost the merchant thousands in customer lifetime value.

Layer three — the reputation cost. Falsely declined customers talk. Social media, review sites, and word-of-mouth all carry the story. The fraud detection layer that blocks a $200 payment can cost the merchant tens of thousands in downstream damage across other prospective customers who hear the story. Vladyslav points out that reputation cost is the hardest layer to measure and the most important to internalise.

"Every merchant knows the cost of fraud because the acquirer sends a bill," Vladyslav Kolodistyi says. "Nobody sends a bill for false declines. So the AI vendor contract keeps rewarding tight tuning and the CFO keeps paying for it in invisible ways."

What the false decline math actually looks like

Vladyslav Kolodistyi has run the numbers with dozens of payment operators. The math is consistent across categories. For most e-commerce merchants running AI fraud detection at typical vendor defaults, the false decline rate sits between three and ten percent of attempted payments. On big-ticket categories like travel and electronics, false declines can exceed fifteen percent. Every one of those false declines is a legitimate customer whose payment was blocked by AI fraud detection.

The economic math then looks like this. If a merchant runs a five percent false decline rate on payment attempts and each declined customer generates half the lifetime value of an accepted customer over the next twelve months, the false decline layer alone destroys more payment revenue than the underlying fraud losses that AI fraud detection was hired to stop.

Vladyslav Kolodistyi argues the math is even worse when the second and third layers are added. Reputation damage compounds. Customer service costs pile up. And the AI fraud detection orchestration keeps exposing what the vendor keeps charging for the models generating the false declines.

How orchestration surfaces the math

Payment orchestration is the layer where the false decline math becomes visible. A single AI fraud detection model at a single acquirer never shows the merchant what would have happened if the payment had been routed differently. Payment orchestration platforms can shadow-test payments, route the same payment through multiple AI fraud detection models, and compare the outcomes. That comparison is how the false decline rate gets measured accurately for the first time.

Vladyslav Kolodistyi has watched this play out with merchants that switch from single-vendor AI fraud detection to multi-model payment orchestration. The pattern is consistent. Within one billing cycle, through the routing platform the merchant discovers that the fraud detection layer has been declining a significant share of good payments. Payment orchestration surfaces the number that AI vendors have every incentive to keep hidden.

"Payment orchestration is the CFO's discovery tool for AI fraud detection false declines," Vladyslav says. "It is the first time the false decline math becomes real. And the first time it becomes real is also the first time the AI fraud detection layer gets tuned to actual payment economics rather than vendor defaults through routing."

The four measurements every merchant should demand

Vladyslav Kolodistyi has assembled a short list of measurements every merchant should demand from a fraud detection vendor or orchestration platform.

The false decline rate on all payment attempts. Not just the payments the system approved. The payments the system blocked, measured against the actual fraud rate on similar transactions.
The customer lifetime value loss attributable to false declines. Vladyslav Kolodistyi keeps emphasising this metric. It is the single most important AI the number no vendor volunteers.
The precision-recall breakdown by payment scenario. Different amounts, categories, and channels have different AI fraud detection profiles. A single blended number hides the false decline math.
The recovery rate on manually reviewed AI declines. If the AI fraud detection layer flags a payment and manual review approves it, that is a real false decline that costs everything above. Track it separately.

"Merchants that make these four measurements standard change the AI fraud detection conversation with their vendors overnight and orchestration data proves it," Vladyslav Kolodistyi says. "The vendors that cannot deliver them get replaced. The merchants that measure them recover payment revenue every quarter."

How orchestration is changing the CFO conversation

Vladyslav Kolodistyi has watched the CFO conversation about AI fraud detection change dramatically over the past two years. Payment orchestration is why. Before payment orchestration, the CFO conversation about AI fraud detection was about the AI vendor's fraud caught number. After payment orchestration, the conversation shifts. The CFO can now see false decline rates, payment revenue lost, and the recovered payment revenue through routing retry.

Vladyslav notes that this changes what CFOs demand from AI contracts. The old contract asked for fraud caught guarantees. The new contract asks for false decline caps. That is a structural change in how AI fraud detection is priced, and it only becomes possible when payment orchestration produces the measurement.

"Payment orchestration is the CFO's AI fraud detection audit trail," Vladyslav Kolodistyi says. "Every payment orchestration platform that ships false decline measurement changes the CFO conversation with AI vendors. The vendors that adapt survive. The vendors that resist get replaced."

The pattern is spreading across payment operator categories. E-commerce merchants adopted payment orchestration first. Travel and marketplace operators followed. Banking and neobank teams are now investing in payment orchestration to solve the same AI fraud detection reporting gap that has been costing them payment revenue for years.

Why AI in payments will fix this eventually

Vladyslav Kolodistyi expects the AI industry to eventually fix the false decline problem, because the economic incentive is too large to ignore. AI in payments platforms with false decline measurement built in will command a premium. Payment orchestration platforms that surface the false decline math will win merchant deployments. AI fraud detection vendors that only report fraud caught will fade.

"The AI fraud detection market is still priced on 2019 assumptions," Vladyslav Kolodistyi says. "The winners in AI in payments over the next five years will be the platforms that make false decline math impossible to hide. Payment orchestration is the orchestration delivery mechanism. Merchants that adopt this framework early recover payment revenue that competitors on legacy AI fraud detection will never see."

PayAdmit provides antifraud and risk management tooling for merchants that want to surface false decline economics before the vendor contract renewal.

About the Vladyslav

Vladyslav Kolodistyi leads payments strategy at PayAdmit, quantifying the AI fraud detection false decline cost through payment orchestration data. Connect with me on LinkedIn for weekly analysis on AI in payments and payment orchestration economics.

Who Settles a Pay by Bank Dispute? Vladyslav Kolodistyi on the Rules UK and EU Regulators Are Writing

Vladyslav Kolodistyi — Tue, 04 Aug 2026 11:15:36 +0000

Every payments industry veteran learns the same rule about card payments: when a dispute arrives, the acquirer settles the argument. The chargeback rulebook is forty years old, and the roles of buyer, merchant, acquirer, and scheme are written into every card payments contract. Pay by Bank has none of this. When a Pay by Bank transaction goes wrong, the industry currently has no single answer to the question of who settles the dispute. That is the regulatory question Vladyslav Kolodistyi from PayAdmit spends most of his week discussing, because it will shape every A2A payments business model for the next decade.

The gap the UK and EU regulators are filling

Card payments have a settled dispute architecture because Visa and Mastercard built it. Pay by Bank has no equivalent private-sector rulebook because Open Banking payments are a public infrastructure sitting on top of banking rails that were never designed for retail dispute handling. The result is a category where the buyer, the merchant, the acquirer, the payments processor, the payments partner, the sending bank, and the receiving bank all have plausible claims about who owes what when Pay by Bank goes wrong.

Vladyslav Kolodistyi maps the current state simply. "In card payments, the acquirer settles disputes because the scheme rulebook says so. In Pay by Bank today, nobody settles disputes because no rulebook exists. The regulators are stepping in because the payments industry has not written one on its own, and Open Banking payments cannot scale to serious purchase flows without it."

The UK Payment Systems Regulator moved first. Since October 2024, the UK requires mandatory reimbursement for authorised push payment fraud across most Open Banking payments rails. The receiving bank and the sending bank split the loss on covered Open Banking payments fraud up to a defined cap. It does not cover the full range of Pay by Bank disputes, but Vladyslav notes it establishes a foundational principle: Open Banking payments liability sits with the banks, not the buyer.

What the European Commission is building for Open Banking payments

The European Commission's Payment Services Regulation, currently working through Parliament, extends the UK approach across the single market and pushes further. The regulation introduces a broader liability regime for Open Banking payments that covers not just fraud but also failed delivery, unauthorised debits, and disputes arising from consent scope violations. Vladyslav Kolodistyi has read every draft. "The EU is essentially writing a European chargeback right for Open Banking payments. It will not be called that, but that is what it is."

Three provisions in the emerging EU framework matter most for anyone building Pay by Bank infrastructure. First, the receiving bank inherits an obligation to hold funds during the dispute intake window, similar to how card payments settlement funds get held pending chargeback resolution. Second, the sending bank inherits an obligation to route dispute notifications to the receiving bank within defined SLAs. Third, the Pay by Bank operator or scheme takes on an obligation to publish dispute statistics, so regulators can monitor A2A payments dispute rates over time.

Vladyslav Kolodistyi argues these three provisions look modest but change the industry. "Each of these obligations pushes Pay by Bank one step closer to card payments parity on the buyer side, without requiring the industry to build a full chargeback scheme from scratch."

How the industry is responding on Open Banking payments

The regulatory rules are only half the story. Vladyslav Kolodistyi tracks the industry response in parallel because rules without operational infrastructure do not protect anyone. His view is that Pay by Bank scheme operators are moving on three fronts:

Building shared A2A dispute infrastructure. Bank consortia in the UK and EU are jointly funding shared Pay by Bank dispute platforms that let sending and receiving banks in real payments time exchange dispute notifications in real time. This is the operational spine the regulators assume exists.
Publishing dispute standards. Open Banking scheme operators are publishing evidence schemas for Pay by Bank disputes. Merchants know what proof of delivery, proof of authorisation, and proof of consent look like across every A2A rail.
Building consumer-facing dispute channels. Vladyslav Kolodistyi keeps repeating that this is where Pay by Bank will win or lose consumer trust. The Open Banking payments dispute channel has to feel as simple as a card dispute button in a mobile app.
Creating cross-border coordination. Open Banking payments cross borders. The Pay by Bank dispute rules cannot stop at national borders. UK and EU regulators are already discussing how a Pay by Bank dispute originating in Ireland but hitting a UK receiving bank gets settled.

Vladyslav Kolodistyi believes the coordination question is the most under-discussed part of the whole Pay by Bank regulatory frontier. "Every regulator focuses on their own border. Open Banking payments do not respect borders. Whoever solves cross-border Pay by Bank dispute coordination first will define the shape of the whole global A2A payments market."

Where Open Banking payments schemes fit in the emerging framework

Vladyslav sees a critical role for Open Banking payments schemes that sits between the regulator and the individual bank. Open Banking scheme operators in the UK and EU are being asked to enforce the emerging dispute framework across participants, publish operational SLAs, and coordinate cross-border A2A payments incidents. That is a substantial expansion of the traditional Open Banking scheme mandate.

"The Open Banking payments schemes have to become dispute enforcement bodies," Vladyslav says. "That is not what they were originally designed for, but it is what the regulators expect them to become." The transition is happening, but Vladyslav notes that the pace varies. UK Open Banking scheme operators are moving faster than their EU counterparts, partly because the UK PSR guidance is more advanced and partly because the UK Open Banking payments market is more concentrated.

Vladyslav argues merchants should track scheme-level Open Banking payments guidance as carefully as they track regulator-level rules. "The regulatory principles are set at the top. The operational rules are set at the scheme level. Merchants that only follow the regulator will miss half the Pay by Bank compliance picture."

Where merchants sit in the new Pay by Bank liability model

The regulatory shift matters most to merchants because it changes their operational obligations. In card payments, merchants have known for decades what they owe when a chargeback lands: proof of delivery, proof of authorisation, and proof of goods. Pay by Bank has never given merchants a comparable checklist, but the emerging regulatory framework is about to.

Vladyslav Kolodistyi argues merchants that treat the emerging Pay by Bank rules as pure compliance overhead will miss the point. "The merchants that read the UK PSR guidance and the EU regulation as design specifications will build better Pay by Bank checkouts. The ones that read them as tick-box exercises will get outcompeted by the ones that treat them as product opportunities."

Vladyslav Kolodistyi points out that the emerging Pay by Bank rules also settle a long-running argument about consent scope. Open Banking payments require explicit consent, and the scope of that consent has been debated since Open Banking launched. The new rules clarify that consent has to be granular, revocable, and audited, which changes how every Pay by Bank checkout has to be built.

What comes next in A2A payments regulation

Vladyslav Kolodistyi expects three developments to shape the next twenty-four months. UK PSR guidance will expand beyond APP fraud to cover a broader set of Pay by Bank disputes. The EU regulation will finalise and enter transition, giving Open Banking payments participants a defined timeline to build operational compliance. And bilateral coordination between UK and EU regulators will formalise cross-border A2A payments dispute handling.

"The regulators are moving faster than most Pay by Bank operators expected," Vladyslav Kolodistyi says. "Every Open Banking payments participant that treats this as a two-year problem will be caught flat-footed. It is a twelve-month problem."

For merchants, the practical implication is straightforward. Pay by Bank dispute infrastructure has to be built now, in parallel with the regulatory finalisation, not after. For fintech operators, the strategic implication is that A2A payments platforms with mature dispute tooling will command a real premium against those without.

PayAdmit works with fintech operators tracking the Open Banking payments regulatory frontier, providing white-label Pay by Bank infrastructure that adapts as the UK PSR and EU rules finalise.

Vladyslav Kolodistyi leads payments strategy at PayAdmit, tracking Pay by Bank regulation across the UK PSR, FCA, and the European Commission. “Follow me on LinkedIn for weekly analysis on Open Banking payments rules and the A2A payments regulatory frontier.”

Speed-to-Market in Payments: Vladyslav Kolodistyi on Why White Label Payment Gateway Beats In-House Build in 2026

Vladyslav Kolodistyi — Wed, 15 Jul 2026 11:03:56 +0000

In this speed-to-market analysis, Vladyslav Kolodistyi from PayAdmit argues that the 12-to-18-month gap between licensing a white label payment gateway and completing an in-house build is the single largest source of payment business performance variance in 2026.

The payment market window race. What happens during 18 months of in-house build while competitors capture share. Visualisation by Vladyslav Kolodistyi / PayAdmit.

Most payment business leaders evaluating a white label payment gateway versus an in-house build focus on cost. Cost matters. Cost is also the wrong primary metric. The variable that actually decides the outcome of most payment businesses is time-to-revenue. The 12-to-18-month gap between white label payment gateway deployment and in-house payment build completion is enough time for the entire competitive landscape to shift. The payment teams that understand this in 2026 are choosing white label payment gateway. The teams that obsess over cost are still building, and watching competitors pull ahead month over month.

I'm Vlad from PayAdmit, a white label payment gateway company serving payment operators across more than 40 markets. Speed-to-market is the variable I see deciding deals every week. Cost arguments tend to lose to capital constraints. Timeline arguments win to revenue urgency. The business case for white label payment gateway becomes self-evident when you put the timeline numbers on paper next to the in-house build numbers.

Why Payment Speed-to-Market Decides Competitive Outcomes in 2026

According to PayRam's 2026 white label payment gateway provider review, an in-house payment build takes 12 to 18 months minimum: development, PCI DSS certification, acquirer integrations, security testing. A white label payment gateway deployment runs 1 to 2 months from contract to live payments transactions. The PayAdmit white label payment gateway typical deployment timeline lands at 6 to 8 weeks. That is not a marginal difference. That is a 10x to 15x difference in time-to-revenue.

Twelve to eighteen months is enough time for new payment acquirers to launch, new payment methods to become standard, payment regulatory rules to shift, and competitors who chose a white label payment gateway approach to capture meaningful customer share. Every month an in-house payment team spends building is a month a white label payment gateway competitor spends optimising, growing, and locking in network effects. The compounding gap is the part that destroys in-house payment timelines.

"Cost matters. Speed-to-market decides. The 12-month gap between white label payment gateway deployment and in-house build is the single largest source of payment performance variance in 2026."
By Vladyslav Kolodistyi

The Compounding Math of Payment Build Delay

The cost of delay does not scale linearly. It compounds. Each quarter a payment business spends in-house building, the gap between that team and a competitor running on a white label payment gateway widens at an accelerating rate. The reason is that the white label payment gateway competitor is not standing still. They are processing payments, learning customer behaviour, training fraud models, building network effects with acquirers, and capturing the prospects who would have been your customers.

The compounding cost of payment delay by quarter. Each quarter of in-house build doubles the cumulative market share loss. Compounding model by Vladyslav Kolodistyi.

The compounding chart above shows the dynamic clearly. Q1 of delay costs roughly 2% of addressable payment market share. Q4 of delay (one full year in) costs 17%. Q6 of delay (the standard end of an in-house build) costs 28% of addressable share. By the time the in-house payment gateway goes live, the white label payment gateway competitor has captured almost a third of the addressable market the in-house team was building to serve.

Q1 (months 0-3): 2% addressable market share lost to faster movers. Recoverable with aggressive launch.
Q2 (months 4-6): 6% share lost. Customer acquisition cost rises as the easy prospects move to competitors.
Q3 (months 7-9): 11% share lost. Network effects start working against the in-house team.
Q4 (months 10-12): 17% share lost. Payment partnerships start locking in with competitors.
Q5 (months 13-15): 23% share lost. The in-house team is now building a product to a market that has moved.
Q6 (months 16-18): 28% share lost. By go-live, the catch-up cost dwarfs the original build savings.

"Each quarter of in-house payment build delay doubles the cumulative market loss. By the time you go live, competitors who chose white label payment gateway have captured a third of the share."
By Vladyslav Kolodistyi

Vladyslav Kolodistyi on Compressing Time-to-Revenue with White Label Payment Gateway Deployment

The white label payment gateway approach compresses time-to-revenue because the heavy work has already been done by the provider. PCI DSS Level 1 environment exists. Acquirer integrations are built. Payment methods are connected. Smart routing logic is configured. Fraud detection is running. The white label payment gateway provider has spent years assembling the payment infrastructure the in-house team is about to spend 18 months trying to replicate.

The PayAdmit white label payment gateway compresses deployment to 6 to 8 weeks because the payment platform was designed for fast deployment. Branded checkout in days. Acquirer connections live in weeks. Custom routing and cascading logic configured per merchant during the same deployment cycle. A white label payment gateway operator can be processing payments and capturing revenue while an in-house team is still scoping the PCI DSS audit.

For payment business leaders evaluating speed-to-market versus cost in 2026, the framework is straightforward. The white label payment gateway license costs more per transaction. The in-house build costs more per quarter of delay. Stack the two costs honestly, and the white label payment gateway wins for any business where time-to-revenue matters more than per-unit transaction economics. For most fintechs, SaaS platforms, marketplaces, and operators, time-to-revenue is the dominant variable.

"The white label payment gateway license costs more per transaction. The in-house build costs more per quarter of delay. For most payment businesses, the delay cost wins."
By Vladyslav Kolodistyi

The competitive position you build in payment infrastructure over the next two quarters defines your payment economics for the rest of the decade. The teams choosing white label payment gateway in 2026 are processing transactions while in-house competitors are still building. The compounding advantage is impossible to recover from a delayed start.

I write about white label payment gateway deployment, payment infrastructure speed-to-market, and the competitive dynamics of payment build decisions regularly. Find me on LinkedIn for the next analysis on payment timeline economics.
Vladyslav Kolodistyi

Banking-as-a-Service After Synapse: Vladyslav Kolodistyi on Building Resilient Embedded Finance in 2026

Vladyslav Kolodistyi — Mon, 13 Jul 2026 15:53:27 +0000

In this post-Synapse analysis, payments infrastructure expert Vladyslav Kolodistyi maps how the Banking-as-a-Service architecture has been rebuilt since the 2024 collapse, and explains what every Embedded Finance team needs to verify before choosing a BaaS partner in 2026.

BaaS architecture before and after Synapse. The pre-2024 fragile stack with BaaS-owned ledger versus the 2026 resilient stack with bank-owned ledger. Architecture comparison by Vladyslav Kolodistyi.

The 2024 collapse of Synapse Financial Technologies was the single largest inflection point in the modern history of Banking-as-a-Service. Over 100,000 end customers lost access to roughly $265 million held across fintech platforms that depended on Synapse for ledger and reconciliation services. The collapse exposed an architectural weakness that almost every early-stage Banking-as-a-Service stack shared. Two years later, the Embedded Finance industry has rebuilt around a fundamentally different BaaS architecture, and the platforms that did not adapt are the ones now struggling for partner banks and regulatory approval.

Having worked in payments infrastructure throughout the post-Synapse rebuild, I have seen the architectural shift play out across dozens of Embedded Finance clients. The platforms that survived and the platforms that grew were the ones that re-architected their Banking-as-a-Service relationships around bank-owned authoritative ledgers, direct customer identification at the sponsor bank, and explicit FDIC pass-through coverage in writing. The platforms that kept the old fragile BaaS architecture have spent the past two years quietly re-papering contracts, switching partners, and rebuilding trust with regulators.

What Synapse's Collapse Taught the Embedded Finance Industry

The Synapse failure modes were not idiosyncratic. They were structural to the pre-2024 Banking-as-a-Service architecture. The BaaS middleware company held the authoritative ledger of end-customer balances. The sponsor bank held only aggregated For-Benefit-Of accounts and could not independently verify individual customer balances. When reconciliation between the BaaS ledger and the bank's view broke down, neither party could establish the truth quickly enough to protect customers. End customers were caught between a BaaS in financial distress and a bank that did not know them as individuals.

As Vladyslav Kolodistyi notes, the structural lesson was that the Banking-as-a-Service middleware should never own the authoritative customer ledger. The sponsor bank should own the ledger and see every end customer directly. The BaaS layer should be a software relay that orchestrates the consumer-facing experience without controlling the underlying record of who owes what to whom. The 2026 Embedded Finance stacks that work all share this architecture. The stacks that do not share it cannot get sponsor banks to onboard them in the current regulatory environment.

Vladyslav Kolodistyi explains that the post-Synapse Embedded Finance architecture also separates regulatory roles more clearly. The sponsor bank owns the compliance burden for the end customer. The BaaS provider owns the software burden for the fintech client. The Embedded Finance platform owns the customer experience burden for the end user. Each party has a clear remit. Each party can be held accountable. Each party knows what the other parties are doing without depending on opaque internal systems. That separation is what makes the 2026 Embedded Finance architecture resilient in ways the pre-2024 architecture was not.

"The structural lesson from Synapse was that Banking-as-a-Service middleware should never own the authoritative customer ledger. The sponsor bank should own it and see every end customer directly."
By Vladyslav Kolodistyi

The New Banking-as-a-Service Governance Stack After 2024

The regulatory response to Synapse reshaped how Banking-as-a-Service oversight works in practice. The OCC and FDIC tightened guidance for sponsor banks running Embedded Finance programmes. Sponsor banks now face stricter capital requirements when offering

BaaS services, more rigorous third-party risk management programmes, and direct supervisory attention on their BaaS books. The downstream impact is that BaaS providers face higher bars to even secure a sponsor bank, and Embedded Finance platforms in turn face higher bars to secure a BaaS partner.

Sponsor bank capital and concentration limits: banks now cap the share of total deposits that come through BaaS programmes, limiting how many fintechs any one bank can serve.
Independent reconciliation audits: third-party verification of ledger accuracy is now standard, replacing self-reporting by the BaaS provider.
Wind-down and resolution planning: every Banking-as-a-Service contract now includes documented procedures for returning customer funds if the BaaS provider fails.
Direct customer identification: end customers must be identified to the sponsor bank, not aggregated into FBO accounts where the bank cannot see individuals.
FDIC pass-through documentation: explicit written confirmation that each end customer's funds are covered up to the $250K insurance limit, with clear paths for proving coverage during a wind-down.

The eight non-negotiable criteria for choosing a Banking-as-a-Service partner in 2026, mapped to the specific Synapse failure modes each criterion addresses. Framework by Vladyslav Kolodistyi.

The eight criteria mapped in the framework above are what serious Embedded Finance teams now verify before signing any Banking-as-a-Service contract. According to Vladyslav Kolodistyi, every one of these criteria addresses a specific failure mode that contributed to the Synapse collapse. Skipping any criterion creates a verifiable governance gap that regulators will flag and that sponsor banks will increasingly refuse to onboard around. The criteria are not optional. They are the entry ticket for modern Embedded Finance partnerships, and Vladyslav Kolodistyi sees them being applied to every new sponsor bank conversation in 2026.

"Every one of the eight 2026 BaaS criteria addresses a specific Synapse failure mode. Skipping any criterion creates a governance gap that regulators flag and sponsor banks refuse to onboard around."
By Vladyslav Kolodistyi

Vladyslav Kolodistyi on Choosing a Resilient Banking-as-a-Service Partner

The practical work of choosing a Banking-as-a-Service partner in 2026 is harder than it was in 2022, and the harder process produces dramatically more resilient Embedded Finance outcomes. The Embedded Finance teams that I see succeeding follow a four-step BaaS partner evaluation: architectural review first, regulatory review second, operational review third, and commercial review last. The order matters. Teams that lead with commercial terms and reverse into architecture end up locked into BaaS contracts that cannot pass post-Synapse regulatory expectations.

Architectural review verifies the BaaS partner uses the bank-owned ledger model. Walk through the data flow. Confirm the BaaS layer does not maintain the authoritative balance record. Confirm end customers are identified at the bank.
Regulatory review confirms the sponsor bank is operating under the post-2024 BaaS oversight regime. Check the primary regulator, the recent supervisory letters, and the bank's history with previous BaaS programmes.
Operational review tests the BaaS partner's actual reconciliation, audit, and dispute resolution processes. Run hypothetical wind-down scenarios. Verify the documented procedures exist and work.
Commercial review negotiates the final BaaS terms once the architectural, regulatory, and operational reviews have passed. Skipping straight to commercial discussions creates the conditions Synapse exposed.

Embedded Finance platforms that follow this evaluation order ship resilient Banking-as-a-Service architectures and pass regulatory scrutiny when it comes. Platforms that skip the early reviews end up making the same architectural choices that Synapse made, and they will face the same kinds of risks Synapse faced once the next stress event hits the Embedded Finance industry. The next stress event is coming. The platforms with bank-owned ledgers, direct customer identification, and documented wind-down plans will navigate it. The platforms that did not rebuild after 2024 will not.

I write about Banking-as-a-Service architecture, Embedded Finance governance, and resilient payment infrastructure regularly. Find me on LinkedIn for the next analysis on BaaS partnership design.
Vladyslav Kolodistyi