DEV Community: Vladyslav Kolodistyi

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

AI Fraud Detection Economics: Vladyslav Kolodistyi on the Real ROI of Payment Orchestration

Vladyslav Kolodistyi — Wed, 01 Jul 2026 14:22:54 +0000

Vladyslav Kolodistyi from PayAdmit works through the actual financial economics of AI fraud detection inside payment orchestration platforms in 2026. The numbers Vladyslav Kolodistyi walks through here are the ones that decide whether the investment pays back or quietly drains margin.

The four cost-and-revenue components of AI fraud detection ROI. Financial framework by Vladyslav Kolodistyi.

Most merchants evaluate AI fraud detection on the wrong number. The vendor demo opens with "AI catches X% more fraud than rules" and the merchant calculates ROI on that single metric. The actual ROI of AI fraud detection inside payment orchestration is four times larger than the fraud-stopped number suggests, because the calculation has to include false decline recovery, chargeback fee avoidance, customer lifetime value protection, and acquirer interchange improvement. Miss any of those four, and the ROI looks far worse than the reality.

Having spent years working in payments infrastructure with global operators, I spend a meaningful share of my time looking at the AI fraud detection developments coming through payment engineering roadmaps and the broader payments ecosystem. According to Mastercard's 2026 AI fraud research, organisations lost $60 million on average to payment fraud last year. But the same research shows 83% of leaders saying AI fraud detection reduced false positives. Those two numbers together tell the real economic story.

The actual ROI of AI fraud detection is four times larger than the fraud-stopped number suggests. Most merchants miss three of the four components.

By Vladyslav Kolodistyi

The Four Economic Components of Payment Orchestration AI Fraud Detection ROI

A complete ROI analysis of AI fraud detection inside payment orchestration has four economic components. Each one moves the calculation in a different direction. Merchants who model only one component are systematically under-investing in AI fraud detection. Merchants who model all four make better payment infrastructure decisions and capture the full economic upside available from payment orchestration.

Component one: direct payment fraud loss reduction. The headline number. AI fraud detection catches more fraud than rules. The capture rate improvement varies by vertical, but a 15-25% reduction in actual payment fraud losses is typical for properly tuned AI inside payment orchestration. For a merchant losing $5M annually to payment fraud, this is $750K to $1.25M in recovered margin per year.
Component two: false decline recovery. The bigger number that most merchants ignore. Every false decline is a lost sale, often a lost customer, and definitely a lost lifetime value. AI fraud detection reduces false declines because it scores transactions on richer signal sets than rules can use. A 30% reduction in false declines on a merchant processing $200M annually with a 5% false decline rate recovers roughly $3M in immediate payment revenue. For high-margin businesses the contribution is closer to $1M of additional profit per year, on top of the direct fraud savings.
Component three: chargeback fee and scheme penalty avoidance. Every chargeback costs the merchant $15-50 in processing fees regardless of dispute outcome. Excessive chargeback ratios trigger acquirer monitoring programmes and increased interchange. Better AI fraud detection inside payment orchestration keeps chargeback ratios below scheme thresholds, avoiding the cumulative penalty cost. For payment-heavy businesses, this component alone can run into six figures per year.
Component four: customer lifetime value protection. The largest and least measured component. A buyer who experiences a false decline is roughly 60% less likely to return within the next twelve months. AI fraud detection that reduces false declines preserves customer lifetime value at scale. For subscription and repeat-purchase businesses, this component frequently dwarfs the other three combined.

The compound savings stack from AI fraud detection inside payment orchestration. Economic model by Vladyslav Kolodistyi.

A buyer who experiences a false decline is roughly 60% less likely to return within twelve months. False decline cost is the largest line nobody calculates.
By Vladyslav Kolodistyi

What AI in Payments Actually Costs to Run at Scale

No serious ROI conversation about AI in payments fraud detection works without an honest look at the cost side. Running production-grade AI in payments architecture is not free, and the cost structure is different from rule-based payment systems. There are four cost lines that every merchant should expect when budgeting AI in payments fraud defence.

First, payment infrastructure cost for the AI in payments scoring engine. Real-time AI fraud detection in payment orchestration requires dedicated compute capacity that scales with payment volume. For most merchants this is a few cents per thousand payment transactions when delivered through a managed payment orchestration platform like PayAdmit, and considerably more when self-hosted. The payment infrastructure cost is real but rarely the dominant line in AI in payments economics.

Second, integration cost. Bringing the AI in payments fraud detection layer into the merchant payment stack requires payment engineering work on signal capture, decisioning hand-off, and feedback wiring. For merchants on a modern payment orchestration platform this is days of work. For merchants on legacy payment stacks it can be weeks. Either way it is a one-time payment integration cost, not a recurring one, and it amortises across years of AI in payments operation.

Third, ongoing tuning and review cost. AI in payments models drift. Threshold tuning, false positive review, and rule overlays all consume operational time. A merchant running AI in payments fraud detection at scale should budget one to two full-time payment fraud analysts whose job is to keep the AI tuned. This is a smaller team than the equivalent rule-based payment fraud operation, but it is not zero.

Fourth, opportunity cost of not deploying AI in payments fraud detection inside payment orchestration. This is the largest cost and the hardest to model. Sumsub's 2026 fraud trends report details how attackers now use generative AI to scale payment fraud operations. A merchant that delays deploying AI in payments fraud defence is paying the opportunity cost of being out-fought by attackers and out-competed by merchants who deployed AI fraud detection inside payment orchestration earlier. This opportunity cost grows every quarter.

The opportunity cost of not deploying AI fraud detection in 2026 grows every quarter. It is the largest cost line and the hardest one to model.

By Vladyslav Kolodistyi from PayAdmit

Putting the four revenue components against the four cost components produces an honest ROI calculation. For most merchants processing more than $50M in annual payment volume, the ROI of AI fraud detection inside payment orchestration runs between 5x and 15x in year one, climbing higher in subsequent years as the AI feedback loop sharpens the model. According to Emburse's 2026 guide to AI fraud detection in banking, agentic AI fraud detection raises that ROI further by automating routine fraud cases that would otherwise consume analyst time.

Vladyslav Kolodistyi on Building a Realistic AI Fraud Detection Business Case

Building a defensible AI fraud detection business case for the CFO is mostly about getting the framework right, not finding optimistic numbers. The framework that consistently survives finance team review has four steps, and each step matters.

Start with measured baselines. The current payment fraud loss rate, false decline rate, chargeback ratio, and average customer lifetime value are the four numbers that anchor the AI fraud detection business case inside payment orchestration. Estimating these is fatal. Measure them.
Use conservative improvement assumptions. The vendor brochure says 30% fraud reduction. The realistic delivered improvement is 15-20% in year one, climbing as the AI fraud detection model sharpens. Model the conservative case for the business case, the optimistic case for the upside.
Include the four economic components separately. Direct payment fraud loss reduction. False decline recovery. Chargeback fee and scheme penalty avoidance. Customer lifetime value protection. CFO trust improves when the four lines are visible separately.
Model the opportunity cost of delay. Every quarter the merchant delays deploying AI fraud detection inside payment orchestration, competitors who deployed earlier pull further ahead and attackers exploit the rules-based defences. This is the line that turns the business case from a maybe into a now.

Most AI fraud detection business cases lose at the CFO because they model one revenue component and ignore opportunity cost of delay.

By Vladyslav Kolodistyi

Done correctly, the AI fraud detection ROI calculation for a payment-serious business reaches numbers large enough that the question is no longer whether to deploy. The question is which payment orchestration platform delivers the AI architecture that captures the full upside fastest. The merchants who get the economics right in 2026 will compound the advantage through 2030.

I write about AI in payments economics and payment orchestration ROI regularly. Connect with me on LinkedIn for the next analysis. The financial case for AI fraud detection in 2026 is stronger than most merchants realise. The cost of waiting another quarter to deploy compounds every month.

Vladyslav Kolodistyi

The Global Real-Time Rails Race: Vladyslav Kolodistyi on How Pay by Bank Is Replacing Cards Across Borders in 2026

Vladyslav Kolodistyi — Fri, 26 Jun 2026 15:22:35 +0000

In this global market analysis, payments expert Vladyslav Kolodistyi maps how six regional real-time A2A rails (FedNow, SEPA Instant, Faster Payments, PIX, UPI, NPP/PayTo) are reshaping cross-border Pay by Bank payments and the competitive position of the global card networks in 2026.

The six regional real-time A2A rails powering the global Pay by Bank takeover in 2026. Map of regional infrastructure by Vladyslav Kolodistyi.

For the first time in fifty years, the global card networks face a credible Pay by Bank infrastructure challenge across multiple major economies simultaneously. The challenge is not a new card network. The challenge is a coordinated rise of real-time account-to-account payments rails in every major payment market: FedNow and RTP in the United States, SEPA Instant in the European Union, Faster Payments in the United Kingdom, PIX in Brazil, UPI in India, NPP and PayTo in Australia. Each of these A2A payments rails is now operating at production scale. Together, they are reshaping where global payment volume flows, and the card networks no longer hold the structural advantage over Pay by Bank that they enjoyed for two generations.

Having worked in payments infrastructure across multiple regions, I have watched this transition accelerate in 2025 and 2026. The interesting part is not that real-time A2A payments rails exist. The interesting part is that they now exist in every major economy at once, each one mature enough for serious cross-border Pay by Bank commerce. The cards-versus-Pay by Bank debate used to be a regional story (PIX in Brazil, UPI in India, niche European Open Banking volume). In 2026 it is a global story, and the global cards networks are competing against a coordinated rise of real-time A2A infrastructure they cannot replicate.

Vladyslav Kolodistyi’s take: The cards networks have not faced this kind of infrastructure competition since the original launch of Visa and Mastercard. Real-time A2A and Pay by Bank rails are not a niche payment method anymore. They are a parallel global payments infrastructure operating at production scale.

Vladyslav Kolodistyi on the Six Real-Time Rails That Define Global A2A Payments in 2026

Each of the six major real-time A2A payments rails has its own scale, its own design, and its own regulatory context. The PIX Pay by Bank rail in Brazil now processes more than 5 billion transactions per month, more than card networks process in the same market, with consumer fees at zero and merchant fees around 0.1% MDR. India's UPI is the largest A2A payments rail in the world by transaction count, processing over 14 billion transactions per month at zero MDR for low-value flows. The combination of free consumer experience and minimal merchant cost made these two Pay by Bank rails dominant in their home markets faster than any payments practitioner predicted.

In the developed economies, the picture looks different but the direction is the same. The UK Faster Payments rail processes 31 million Open Banking payments per month with 60.5% of UK adults using Open Banking, according to SQ Magazine's 2026 Open Banking adoption analysis. SEPA Instant in the EU is now mandated at parity pricing with standard SEPA under the Instant Payments Regulation, meaning real-time A2A and Pay by Bank payments no longer carry a premium over slower bank transfers. FedNow and RTP in the US have crossed $1.2 trillion in processed payments value in 2025, and the CFPB's Section 1033 rule taking effect in April 2026 (per Digital API's 2026 Open Banking trends report) opens the regulatory door to mainstream Pay by Bank adoption in the world's largest economy. Australia's NPP and PayTo combination delivers VRP-style recurring billing natively, the closest international peer to UK Commercial VRPs.

Vladyslav Kolodistyi notes that the global rails picture is not about which rail wins. It is about the fact that every major economy now has a credible real-time A2A and Pay by Bank alternative to cards, which collectively reshapes global payment economics. A merchant operating in Brazil, the UK, India, and the EU can route domestic payments through local A2A and Open Banking rails and avoid card fees on the largest share of their volume. The card networks still serve cross-border tourist transactions and unbanked consumers, but the share of global payment volume they touch is now structurally smaller than it was even five years ago.

"Every major economy now has a credible real-time Pay by Bank alternative to cards. The card networks still serve tourist transactions and unbanked consumers, but the share of global payment volume they touch is structurally smaller."
By Vladyslav Kolodistyi

Vladyslav Kolodistyi’s take: I tell merchants to think of cards as a layer in their payment stack, not the foundation of it. The foundation in 2026 is the local A2A and Open Banking rail of each market they operate in. Cards sit on top for the corridors where Pay by Bank does not work.

Cross-Border Payments: Where Open Banking Payments Win and Where Cards Still Hold the Niche

The cross-border picture in 2026 has six common corridors that almost every global merchant encounters. The economics of cards versus local A2A rails differ dramatically across these corridors. The chart below maps the comparison: cost, approval rate, and settlement time for both payment methods in each corridor. The result is consistent. A2A and Pay by Bank win five out of six corridors decisively. Cards retain one important niche: tourist or expat transactions where the buyer does not have a local bank account to authenticate against the merchant's A2A payments rail.

Cross-border payment corridor comparison. Cards versus local A2A rails across six common scenarios. A2A wins five, cards win one. Analysis by Vladyslav Kolodistyi.

Look at the UK SME to German supplier corridor as a representative example. A £15,000 invoice paid via card costs the merchant £300 to £450 in processing fees, faces 60-75% approval rates due to high-value flags, and settles between T+2 and T+5. The same invoice paid via A2A and Pay by Bank rails (UK Faster Payments out, SEPA Instant in, with FX handled by an Open Banking layer) costs around £1.50 fixed, sees 99%+ approval rates, and settles in seconds. The card path is not just more expensive in absolute terms. It is structurally worse across every dimension that matters to a finance team, and the cost compounds with every transaction.

The one corridor where cards still win is the unbanked or non-local-banked consumer. A European tourist buying from a US merchant has no UK Faster Payments account to push from. The merchant's SEPA Instant integration cannot authenticate against the tourist's home bank if that bank does not participate. Cards retain this niche because they are the universal fall-back when local A2A and Open Banking rails do not span the buyer's banking relationship. For most merchants, this corridor is a small share of total volume but a non-zero share, which is why the smart 2026 payment strategy is to offer both Pay by Bank and cards, not to replace one with the other.

Vladyslav Kolodistyi’s take: The right 2026 payment stack is not Pay by Bank only. It is Pay by Bank first, cards as fall-back for the corridors Open Banking payments cannot span. That asymmetric design captures the margin benefits while protecting the rare niche where cards still win.

Merchants who structure their payment routing around this hierarchy in 2026 are seeing meaningful margin recovery, faster settlement, and dramatically lower chargeback exposure. Merchants who keep cards as the default and treat A2A payments as an afterthought continue to absorb costs that competitors are eliminating. The global real-time rails race is no longer about whether Pay by Bank is viable. It is about how fast each merchant can restructure their payment stack to capture the savings that the rails make available.

I write about global A2A payment rails, cross-border payment infrastructure, and the cards-versus-Pay by Bank competitive dynamics regularly. Follow Vladyslav Kolodistyi on LinkedIn for the next global payments analysis.

"The global real-time rails race is no longer about whether Pay by Bank is viable. It is about how fast each merchant restructures their payment stack to capture the savings."
By Vladyslav Kolodistyi