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10 Fintech & Payments Trends Reshaping Financial Services in 2026

reben002 — Sat, 04 Apr 2026 10:53:13 +0000

Payments · Infrastructure · Banking Technology

10 Fintech & Payments Trends Reshaping Financial Services in 2026

From NFC wallet wars and virtual card adoption to behavioural biometrics and pan-European instant payment networks — the infrastructure decisions that banks, acquirers, and fintech operators need to make this year. A Virginia Heritage analysis.

Virginia Heritage Editorial · April 2026 · 18 min read

$11.4T

eCommerce by 2029

B2B Payments via Virtual Card

99%

US Merchant Acceptance (4th Network)

75%+

EU Citizens Eligible for Pan-EU Wallets

Why 2026 Is a Structural Inflection Point

Financial services infrastructure is being rebuilt in real time. The forces driving this are not abstract — they are regulatory mandates that require technical compliance by fixed deadlines, competitive dynamics that are eliminating the moats traditional banks relied on, and fraud patterns that are evolving faster than manual defences can respond. What distinguishes this cycle from previous waves of fintech hype is that the changes are infrastructure-level: NFC access policies, real-time settlement networks, biometric authentication layers, and API quality standards mandated by law.

This guide maps the ten most consequential trends across payments, identity, compliance, and digital banking — not as isolated developments, but as interconnected shifts that are reshaping the competitive landscape for banks, acquirers, payment processors, and the fintech operators building on top of them. For each trend, we examine the underlying driver, the infrastructure impact, and the strategic implication for financial institutions navigating this transition.

The 10 Trends at a Glance

#	Trend	Category	Impact
1	NFC Access Opens Up the Digital Wallet Market	Payments	🔴 High
2	Virtual Cards Redefine Corporate Expense Management	B2B Payments	🔴 High
3	Behavioural Biometrics Enable Passive Authentication	Identity & Security	🔴 High
4	Merchants Adopt Localised Global Payment Strategies	eCommerce	🟠 Medium–High
5	Regtech Accelerates in Response to BaaS Compliance Failures	Compliance	🔴 High
6	Next-Generation Open Banking Regulation Forces API Upgrades	Regulation	🟠 Medium–High
7	Vertical Integration Challenges the Card Network Duopoly	Card Networks	🟠 Medium–High
8	Pan-European Instant Payment Networks Gain Traction	Instant Payments	🔴 High
9	AI Investment Shifts from Hype to Fraud & Identity Infrastructure	AI & ML	🟠 Medium–High
10	Sustainability-Embedded Banking Becomes a Competitive Lever	Digital Banking	🟡 Medium

Trend 1

NFC Access Liberalisation Is Rewriting the Digital Wallet Competitive Map

For years, the near-field communication chip inside smartphones has been a gated resource. On one major mobile operating system, the NFC capability was available to any developer. On the other, access was restricted exclusively to the device manufacturer’s own wallet application — creating a de facto monopoly on in-store contactless payments for hundreds of millions of devices worldwide.

Regulatory pressure, particularly from the European Union, has forced this open. Third-party developers can now build NFC-enabled payment functionality on previously locked devices — initially in Europe, and expanding across North America, Asia-Pacific, and Latin America. The implications are structural, not cosmetic.

The question is no longer whether the wallet market becomes competitive — it already has. The question is how quickly banks, fintechs, and retailers build NFC payment experiences that give consumers a reason to switch.

Three categories of entrants are now positioned to compete in the in-store contactless payments market. First, existing digital wallet providers that previously operated only in the online or peer-to-peer space can now offer tap-to-pay at physical terminals. Second, card issuers and banks can build their own branded wallet experiences, keeping the customer within their ecosystem and retaining transaction revenue that previously flowed to device-linked wallets. Third, large retailers — many of which already operate closed-loop QR-code payment systems — can migrate those systems to NFC, converting closed-loop ecosystems into open-loop ones.

Before NFC Liberalisation

One wallet per device ecosystem. Banks pay the device manufacturer for tap-to-pay access. No competitive pressure on wallet UX or fees.

After NFC Liberalisation

Multiple wallets on every device. Banks can bypass intermediaries. Retailers and fintechs enter the in-store payments market. Fee compression accelerates.

Digital Wallet Penetration by Region — Estimated % of Population (2025–2026)

Far East & China88%

Indian Subcontinent62%

North America54%

Western Europe48%

Latin America38%

Africa & Middle East22%

Source: Industry estimates, Virginia Heritage analysis

The highest-impact regions will be Western Europe and North America, where wallet penetration is meaningful but not saturated and where regulatory mandates are most advanced. In Far East and China, where wallet adoption already exceeds 85 percent of the population, the effect will be muted because NFC is less central to the dominant payment flows. The implication for community and regional banks: this is a window to launch issuer-branded wallet experiences that retain transaction economics — but the window will not stay open indefinitely.

Trend 2

Virtual Cards Are Replacing Physical Expense Cards — And the Economics Are Compelling

Physical corporate cards have been the default mechanism for employee expenses for decades, and they carry well-understood risks: deliberate misuse, accidental overspending, card loss, and credential theft. Virtual cards address every one of these failure modes through programmable constraints — spend limits, transaction limits, merchant restrictions, single-use credentials, and real-time visibility into where and how money is being spent.

Capability	Physical Card	Virtual Card
Instant issuance	✗	✓
Per-transaction spend caps	✗	✓
Single-use credentials	✗	✓
Merchant-category restrictions	Limited	✓
Real-time spend visibility	✗	✓
Risk from physical loss / theft	High	Eliminated

Global B2B Payment Value by Channel (2025–2026 estimate)

32%

Instant Payment

27%

Other Digital

20%

Wire Transfer

Virtual Card ↑

Virtual cards now account for more B2B payment value than cash or cheques for the first time. Source: Industry estimates

The adoption curve is being led by large enterprises with dedicated expense management teams that currently spend significant hours reconciling physical receipts. Virtual cards replace that manual process entirely — spending data flows automatically into accounting systems, itemised and categorised. The direct savings come from reduced fraud. The indirect savings come from freeing accounting staff to do higher-value work. For banks and card issuers, offering embedded virtual card issuance as a BaaS capability is becoming a baseline expectation from corporate treasury teams.

Trend 3

Behavioural Biometrics Are Making Authentication Invisible — And That Is the Point

Static biometrics — a fingerprint scan, a face recognition check — authenticate a user at one moment in time. Once that gate is passed, the system has no ongoing visibility into whether the person using the account is still the person who authenticated. Behavioural biometrics change this by continuously analysing how a user interacts with their device: typing cadence, swipe pressure, mouse movement patterns, navigation habits, and session timing. Every user produces a unique behavioural signature, and deviations from that signature can be flagged in real time without interrupting the user experience.

⌨️

Keystroke Dynamics

Typing speed, pressure, rhythm between key presses

📱

Touch & Swipe Patterns

Swipe velocity, finger angle, pressure distribution

🖱️

Navigation Behaviour

Mouse trajectory, scroll habits, session flow

This is not a standalone technology — it works best as a layer on top of existing authentication, creating a multi-modal system that combines what the user knows (password), what the user has (device), what the user is (face or fingerprint), and how the user behaves (behavioural signature). Financial institutions are investing in this approach specifically to combat account takeover fraud and social engineering, where a legitimate session is hijacked after initial authentication. The value proposition for banks is clear: better fraud detection performance with less friction, which means fewer false-positive blocks, fewer customer complaints, and lower operational cost in the fraud operations team.

Trend 4

Localised Global Payments: The ‘Glocal’ Imperative for Cross-Border Commerce

Global eCommerce is forecast to grow from approximately $7 trillion to $11.4 trillion over the next five years, at a compound rate of nearly 10 percent. Much of that growth is coming from emerging economies where consumer payment preferences are sharply localised — mobile money in East Africa, account-to-account transfers in Brazil, unified payment interfaces in India, wallet-based payments across Southeast Asia. International merchants that do not offer the locally preferred payment method at checkout lose the sale. Payment orchestration platforms have become the critical infrastructure layer, routing transactions through the lowest-cost, highest-authorisation-rate path across multiple providers, currencies, and regulatory regimes.

Global eCommerce Market

Projected growth trajectory

$7T

2024

→

$11.4T

2029

Trend 5

Regtech Is Being Rebuilt Because Banking-as-a-Service Broke It

The banking-as-a-service model has had a turbulent regulatory reckoning. Multiple sponsor banks have received enforcement actions for deficiencies in anti-money laundering controls, third-party risk management, and consumer protection when partnering with fintech intermediaries. The collapse of at least one major BaaS middleware platform left customer funds in limbo and raised fundamental questions about the model’s viability. But the problem was never the BaaS concept itself — it was the compliance infrastructure underneath it. Regulatory expectations for AML, KYC, and ongoing transaction monitoring across multi-party ecosystems require a level of automation, transparency, and real-time oversight that manual processes cannot deliver. Next-generation regtech platforms are closing this gap with configurable compliance engines, continuous monitoring across the full customer lifecycle, and third-party risk management tools that assess exposure beyond immediate suppliers.

Trend 6

Next-Generation Open Banking Regulation Is Forcing Real API Quality — Not Just API Availability

The first generation of open banking regulation mandated that banks provide API access to account data and payment initiation. The next generation is going further: specifying minimum API functionality, latency requirements, stronger authentication standards, and harmonised rules that eliminate the national variations which have fragmented the European market. For banks, this means material technical investment — improving API performance, meeting tougher sanctions for non-compliance, and preparing for an expanded payments ecosystem where non-bank providers have regulatory parity. Open banking users in Western Europe are projected to grow from approximately 130 million to over 250 million by the end of the decade. The banks that treat this as a compliance obligation will lose ground. The banks that treat it as a platform opportunity will capture revenue from embedded financial services flowing through third-party applications.

Open Banking Users — Western Europe (millions, estimated)

2021

2022

2023

130

2024

170

2025

205

2026

255

2029

Source: Industry estimates, Virginia Heritage analysis

Trend 7

Vertical Integration Is the Only Credible Threat to the Card Network Duopoly

The global card network market has been dominated by two players for decades, with smaller networks occupying specialist niches. The structural challenge has always been that issuers and networks are separate entities — issuers pay the network for routing and acceptance, and the network captures a margin on every transaction. When a major card issuer acquires a card network, it creates a vertically integrated entity that controls both sides of the transaction. The combined entity gains efficiencies that competitors cannot match: it can offer more aggressive reward programmes funded by eliminated network fees, or lower merchant pricing that drives acceptance. The critical question is migration timing — moving existing cardholders from established networks to the acquired network without disrupting service or losing international acceptance coverage. US merchant acceptance for the fourth-largest card network now exceeds 99 percent, which removes the historical objection that smaller networks cannot deliver sufficient coverage.

Annual Credit Card Issuance — Share by Region (2025–2026)

North America		24%
Far East & China		45%
Latin America		10%
Western Europe		6%
Rest of World		15%

Source: Industry estimates

Trend 8

Pan-European Instant Payments Are Finally Becoming a Real Alternative to Card Networks

The European payments landscape has lacked a homegrown digital payment alternative to the dominant US-based card networks and wallets. That is changing. Pan-European instant payment wallets — built around account-to-account transfers triggered by phone number rather than card credentials — have launched across multiple major EU markets. With over 30 partner banks covering more than 75 percent of the eligible population in initial launch countries, these systems are purpose-built to offer sovereign payment alternatives for peer-to-peer transfers, and are roadmapped to expand into merchant payments, point-of-sale transactions, and value-added services like buy-now-pay-later backed directly by banks.

The EU’s instant payment regulation, which took effect in early 2024, ensures that all EU citizens and businesses with a bank account can send and receive instant payments more conveniently and cheaply than before. Combined with open banking developments facilitating account-to-account payments, the volume of consumer A2A transactions in Europe is projected to grow substantially through the end of the decade. The strategic significance for banks: participating in these networks is not optional. The institutions that integrate early will shape the standards. Those that wait will inherit them.

Trend 9

AI in Banking Is Moving Past the Hype Cycle — Fraud and Identity Are Where It Actually Works

The initial wave of AI enthusiasm in financial services spread investment thinly across too many use cases without clear objectives. Banks launched AI projects as short-term experiments rather than building the data infrastructure and feedback loops that allow machine learning models to improve over time. The result was disillusionment with broad AI initiatives — and a sharpening of focus toward the domains where AI has already demonstrated measurable value: fraud detection and identity verification.

Anomaly Detection

AI models identify transaction patterns that deviate from established baselines, flagging potential fraud before losses accumulate across channels.

Generative AI Defences

New defensive models counter deepfake-powered social engineering by detecting synthetic media in real-time identity verification flows.

Network-Level Analysis

AI analyses transaction flows at a systemic level, detecting coordinated fraud rings and mule networks that single-transaction analysis would miss.

Adaptive Learning

Models continuously retrain on new fraud patterns, reducing the lag between emerging attack vectors and defensive capability — unlike rule-based systems.

The longer-term trajectory is clear: financial institutions will eventually expand AI to customer-facing applications, but the highly regulated and risk-averse nature of banking means that internal operations — fraud prevention, compliance automation, and identity management — will remain the primary deployment domain for years to come. Banks that invest now in the data infrastructure and model governance frameworks required for effective AI-powered fraud detection will have a significant head start when they are ready to extend AI capabilities outward.

Trend 10

Sustainability-Embedded Banking Is Becoming a Real Differentiator — Not Just a Marketing Exercise

Consumer awareness of the environmental and social impact of financial services has reached a threshold where sustainability-focused offerings are influencing banking platform selection — particularly for long-term financial decisions like investing and savings. Digital banks and challenger institutions are embedding ESG principles directly into their product experience: carbon footprint calculators tied to spending categories, micro-offsetting programmes linked to individual transactions, subscription services funding verified climate projects, and marketplace models that give customers granular control over which environmental initiatives they support. For community banks, the question is not whether to offer sustainability features — it is which model delivers genuine impact while integrating cleanly with existing infrastructure.

Model	How It Works	Best For
Carbon Footprint Calculator	Categorises spending and calculates environmental impact per transaction	Awareness & engagement
Micro-Offsetting	Rounds up or adds a small offset charge per transaction to fund carbon credits	High-volume retail customers
Project Subscription	Monthly subscription funding solar, reforestation, or conservation projects with progress tracking	Long-term impact seekers
Climate Action Marketplace	Pay-as-you-go marketplace where customers choose and fund individual environmental projects	Engaged, high-net-worth customers

What These Trends Mean for Community Banks and Regional Institutions

The common thread across all ten trends is that financial services infrastructure is being rebuilt around principles of openness, real-time processing, continuous authentication, and programmable control. Institutions that historically competed on relationship depth and community presence now need to compete on technology execution as well — not because technology replaces relationships, but because the infrastructure layer determines who can deliver financial products efficiently, safely, and at scale.

The encouraging signal for smaller institutions: many of these trends are accessible via platform partnerships and BaaS integrations rather than requiring in-house development. Virtual card issuance, behavioural biometrics, regtech compliance engines, and instant payment network participation can all be deployed through well-governed partnerships. The institutions that act on this signal in 2026 will be the ones best positioned to absorb whatever the next wave of infrastructure change demands.

Frequently Asked Questions

What does NFC access liberalisation mean for consumers?

It means that consumers will no longer be locked into a single digital wallet dictated by their device manufacturer. Multiple wallet applications — from banks, payment companies, retailers, and fintech providers — will be able to offer tap-to-pay functionality on the same device, giving consumers more choice and driving competition on features, rewards, and fees.

How do virtual cards reduce corporate expense fraud?

Virtual cards can be issued with programmable constraints: per-transaction spend caps, single-use credentials that expire after one payment, merchant category restrictions, and total spend limits. This eliminates the most common fraud vectors associated with physical corporate cards — including misuse, credential theft, and unauthorised spending at non-approved merchants.

What is the difference between static and behavioural biometrics?

Static biometrics authenticate a user at a single point in time — a fingerprint scan or face recognition check. Behavioural biometrics continuously monitor how the user interacts with their device (typing patterns, swipe behaviour, navigation habits) to detect anomalies throughout the entire session. The two work best together as layers in a multi-modal authentication system.

What is a payment orchestration platform?

A payment orchestration platform is a middleware layer that manages payment routing across multiple providers, payment methods, currencies, and countries through a single API integration. It uses smart routing to direct each transaction through the lowest-cost path with the highest authorisation rate, handles failover routing, currency conversion, and compliance with local regulations — all from a centralised dashboard.

Why has Banking-as-a-Service faced regulatory scrutiny?

BaaS models create multi-party ecosystems where compliance responsibilities can become unclear. Several sponsor banks that provide the banking charter underlying BaaS programmes have received enforcement actions for insufficient anti-money laundering controls, inadequate third-party risk management, and misleading consumers about the protection status of their deposits. The issue is not the BaaS concept but the immaturity of the compliance infrastructure supporting it.

What are the next-generation open banking regulations trying to fix?

The first generation mandated API access but did not specify quality. The next generation addresses this by requiring minimum API functionality, performance benchmarks, latency standards, stronger authentication rules, and harmonised implementation across member states to eliminate the national variations that fragmented the market.

How does vertical integration in card networks affect merchants?

When an issuer owns the network, it eliminates the network fee that issuers normally pay. Those savings can be passed to merchants through lower interchange rates, making the vertically integrated network more attractive for merchant acceptance. It can also fund more aggressive cardholder reward programmes, which drives consumer adoption and, in turn, merchant acceptance.

What is the significance of phone-number-based payment networks?

Phone-number-based payment systems eliminate the need to share bank account details or card credentials between parties. Users register their phone number once, and payments can be sent and received using only that number. This simplifies cross-border payments within participating networks, reduces friction in peer-to-peer transfers, and creates a foundation for instant merchant payments that bypass traditional card rails entirely.

Why is AI more effective for fraud detection than for general banking applications?

Fraud detection benefits from characteristics that make AI particularly effective: large volumes of structured data, clear success metrics (fraud caught vs. missed), and the ability to run models continuously with automated feedback loops. General banking AI applications — like customer service or product recommendation — involve more ambiguous success criteria, higher regulatory risk, and customer-facing interactions where errors have reputational consequences. The data infrastructure that fraud teams have built over years gives AI the foundation it needs to perform well.

How can community banks participate in these trends without large technology budgets?

Most of the infrastructure capabilities described in this guide — virtual card issuance, behavioural biometrics, regtech compliance engines, payment orchestration, and instant payment network participation — are available as platform services and API integrations. Community banks do not need to build these capabilities in-house. The strategic decision is selecting well-governed partners, negotiating appropriate data-sharing terms, and ensuring that third-party integrations are subject to the same risk management oversight that regulators now expect across the entire BaaS and fintech partnership ecosystem.

Virginia Heritage is an independent financial technology publication. This analysis is editorial commentary and does not constitute financial or investment advice. Data points are sourced from publicly available industry estimates and are presented for analytical context. © 2026 Virginia Heritage. All rights reserved.

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The Ultimate Fintech Glossary 2026: 60+ Terms Every Banking and Financial Technology Professional Needs to Know

reben002 — Sat, 04 Apr 2026 10:51:24 +0000

Financial technology moves fast — and its vocabulary moves even faster. Every quarter brings new acronyms, regulatory frameworks, and product categories that banking executives, compliance officers, and fintech builders need to understand. The problem is not a shortage of definitions. It is that most glossaries treat fintech terms as static dictionary entries, stripped of the context that makes them useful for practitioners making real decisions.

This glossary is different. We have assembled over 120 terms spanning payments, lending, compliance, AI, blockchain, open banking, and regulatory technology — and written each definition with working professionals in mind. Whether you are a community bank CTO evaluating a core banking migration, a compliance lead implementing new AML automation, or a fintech founder building on open banking rails, these definitions are designed to give you clarity you can act on — not just awareness you can nod along with.

Use the alphabetical navigation below to jump directly to the section you need, or read through for a comprehensive education in the language of modern financial services.

Jump To Section

A B C D E F G H I K M N O P R S T V

A

Account-Based Payment Instrument

Any payment mechanism that draws on funds held in a formal account — whether that is a traditional bank deposit, a prepaid e-money balance, or even an account-based central bank digital currency (CBDC). The defining characteristic is the existence of an institution that maintains a ledger of who owns what. This distinguishes account-based instruments from token-based systems (like certain cryptocurrency architectures) where possession of a cryptographic key, rather than an account record, determines ownership.

Agentic Commerce

A 2025–2026 concept describing transactions initiated and completed by AI agents acting on behalf of a consumer or business. Unlike traditional e-commerce where a human clicks “buy,” agentic commerce involves an AI assistant researching products, comparing prices, and executing purchases autonomously within parameters set by the user. The payment infrastructure implications are significant — existing checkout flows, authentication protocols, and fraud detection systems were not designed for non-human buyers.

Algorithmic Decision-Making

The practice of using data inputs and statistical models to automate or inform decisions that were traditionally made by humans. In financial services, algorithmic decision-making powers everything from loan approvals and insurance pricing to fraud detection and payment authorisation. The degree of human involvement varies — some systems flag decisions for human review, while others operate autonomously within defined parameters. Regulatory scrutiny of algorithmic decision-making has intensified, particularly around questions of bias, explainability, and accountability when automated systems produce adverse outcomes for consumers.

AML/CFT (Anti-Money Laundering / Countering the Financing of Terrorism)

The collective body of laws, regulations, supervisory actions, and enforcement procedures designed to prevent, detect, and prosecute the use of the financial system for money laundering or terrorism financing. AML/CFT compliance is one of the most resource-intensive obligations for any financial institution — and one of the areas where regtech and AI are having the most measurable impact. The Financial Action Task Force (FATF) sets the international standards that national regulators implement, and non-compliance carries severe consequences including fines, licence restrictions, and criminal prosecution of responsible officers.

Anti-Tying Rules

Regulations that prohibit financial institutions from conditioning the provision of one product or service on the customer purchasing another product from the same provider — or from refusing to serve a customer who obtains similar products from a competitor. Anti-tying rules are designed to prevent incumbents from using bundling strategies to lock customers into their ecosystem and are particularly relevant in the open banking era, where regulators want to ensure that customers can freely share their data with third-party providers without being penalised by their primary bank.

API (Application Programming Interface)

A standardised set of rules and protocols that allows different software systems to communicate with each other. In financial services, APIs are the connective tissue that makes modern banking work — enabling everything from mobile banking apps pulling account balances to open banking platforms initiating payments on behalf of customers. The quality, reliability, and security of a financial institution’s APIs increasingly determine its competitive position. Under regulations like PSD2 in Europe and proposed Section 1033 rules in the United States, banks are required to make customer data available to authorised third parties through standardised APIs.

Artificial Intelligence (AI)

Computer systems designed to perform tasks that traditionally required human intelligence — pattern recognition, natural language understanding, decision-making under uncertainty, and learning from experience. In banking and fintech, AI applications span credit underwriting, fraud detection, transaction monitoring, customer service automation, document processing, and regulatory reporting. The deployment of AI in financial services is increasingly governed by specific regulations, including the EU AI Act (effective 2024) and emerging U.S. guidance on model risk management for machine learning systems. The distinction between narrow AI (purpose-built for specific tasks) and general-purpose AI models (like large language models) is critical for understanding both the capabilities and limitations of current financial AI applications.

Asset-Based Lending (ABL)

Lending products where the loan is secured against movable property — equipment, inventory, receivables, invoices, or intangible assets like intellectual property. ABL encompasses several distinct products including factoring (selling receivables), reverse factoring (supply chain finance), revolving credit lines secured by changing collateral pools, merchant cash advances, and equipment leasing. Fintech platforms have made ABL more accessible to smaller businesses by automating asset valuation, collateral monitoring, and covenant compliance through real-time data integration.

Automated Clearing House (ACH)

An electronic network for batch-processing financial transactions between banks and financial institutions. In the United States, the ACH network (operated by Nacha) handles billions of transactions annually — payroll direct deposits, recurring bill payments, government benefit distributions, and business-to-business transfers. ACH processes transactions in batches rather than individually, which means settlement is not instantaneous; same-day ACH has reduced but not eliminated settlement delays. The ACH network remains one of the most important pieces of U.S. payment infrastructure despite the emergence of real-time alternatives like FedNow and RTP.

B

Banking-as-a-Service (BaaS)

A model in which a licensed bank provides its regulatory infrastructure, core banking systems, and compliance framework to non-bank companies through APIs — enabling those companies to offer banking products (accounts, cards, payments, lending) under their own brand without obtaining a banking licence. The BaaS model has faced significant scrutiny from regulators in 2024–2026, with multiple enforcement actions against both sponsor banks and their fintech partners over compliance failures, particularly in AML and consumer protection. The economics of BaaS are also under pressure, as the operational cost of managing partner compliance has proved higher than many early models assumed.

Basel Core Principles (BCPs)

The global minimum standards for prudential regulation and supervision of banks, published by the Basel Committee on Banking Supervision. First issued in 1997 and updated in 2006 and 2012, the BCPs cover areas including licensing, capital adequacy, risk management, governance, and supervisory powers. For fintech companies building on bank infrastructure (through BaaS or embedded finance models), the BCPs indirectly define the compliance requirements they must satisfy — because the bank they partner with is supervised against these standards.

Big Data

The vast volumes of structured and unstructured data generated through digital interactions, financial transactions, device telemetry, and online activity. In financial services, big data enables alternative credit scoring (using non-traditional data points to assess creditworthiness), real-time fraud detection (identifying anomalous patterns across millions of transactions), personalised product recommendations, and market risk modelling. Big tech companies — firms like Apple, Google, and Amazon that have expanded into financial services — derive a significant competitive advantage from the scale and richness of their customer data, which often exceeds what traditional banks collect.

Blockchain

A specific type of distributed ledger technology (DLT) that organises transaction records into sequential, cryptographically linked blocks. Each new block references the hash of the previous block, creating an append-only chain that is extremely difficult to alter retroactively. In financial services, blockchain applications include cross-border payment settlement, trade finance documentation, securities tokenisation, and the infrastructure underlying cryptocurrencies and stablecoins. The practical distinction between public blockchains (permissionless, like Ethereum) and private/permissioned blockchains (like those used by consortia of banks) is critical for understanding the security, performance, and governance characteristics of different implementations.

C

Central Bank Digital Currency (CBDC)

A digital form of sovereign currency issued directly by a central bank. Unlike commercial bank deposits (which are liabilities of private banks) or stablecoins (which are liabilities of private issuers), a CBDC carries the direct backing of the central bank. CBDCs can be designed for retail use (general public transactions) or wholesale use (interbank settlement). As of 2026, the digital euro is in advanced preparation by the European Central Bank, while China’s e-CNY has been in pilot since 2020. The United States has taken a more cautious approach, with the Federal Reserve exploring the concept through research and limited pilots without committing to issuance.

Challenger Bank

A newly licensed bank that competes against established incumbent institutions, typically by offering a differentiated customer experience, lower fees, or technology-first service delivery. The term originated in the UK market and is distinct from “neobank” — a challenger bank holds its own banking licence and takes deposits directly, while a neobank typically operates through a partner bank’s licence. Examples include Starling Bank and Monzo in the UK. Many challenger banks have struggled to achieve profitability, and the term itself has fallen somewhat out of favour as the survivors have matured into established institutions in their own right.

Cloud Computing

The delivery of computing resources — servers, storage, databases, networking, analytics, and software — over the internet rather than through locally hosted infrastructure. For financial institutions, cloud migration represents one of the most consequential technology decisions of the current decade. Cloud-native core banking platforms (like Thought Machine, Mambu, and 10x Banking) allow institutions to scale infrastructure dynamically, deploy updates continuously, and reduce the capital expenditure of maintaining legacy data centres. Regulatory guidance on cloud adoption in financial services has evolved from cautious to broadly permissive, though requirements around data residency, concentration risk, and third-party risk management remain significant.

Core Banking System

The central software platform that manages a bank’s fundamental operations — maintaining the ledger of accounts, processing transactions, calculating interest, managing deposits and loans, and providing the system of record that every other banking application depends on. Core banking systems are often decades old, written in COBOL or other legacy languages, and represent the single largest constraint on a bank’s ability to innovate, integrate with modern APIs, and offer real-time services. Core banking transformation — replacing or modernising these systems — is one of the most complex and risky technology programmes a financial institution can undertake, often taking three to five years and consuming hundreds of millions of dollars in the largest institutions.

Correspondent Banking

An arrangement where one bank (the correspondent) holds deposits and provides payment services on behalf of another bank (the respondent), typically to facilitate cross-border transactions. Correspondent banking has been the backbone of international payments for over a century but is under pressure from multiple directions: de-risking (where correspondent banks cut relationships with smaller or higher-risk respondent banks), competition from fintech-enabled cross-border payment services, and the emergence of real-time cross-border settlement networks that bypass the traditional correspondent chain entirely.

Credit Scoring

A statistical methodology for evaluating the likelihood that a borrower will repay a loan. Traditional credit scoring relies on bureau data — payment history, outstanding balances, length of credit history, and types of credit used. Alternative credit scoring, enabled by open banking and big data, incorporates non-traditional data sources like rent payments, utility bills, bank transaction patterns, and even social or behavioural data to assess creditworthiness. AI-driven credit scoring models can evaluate thousands of variables simultaneously, but face regulatory scrutiny around explainability, bias, and the right of consumers to understand why they were denied credit.

Cross-Border Payments

Financial transfers where the sending and receiving parties are located in different countries. Cross-border payments may or may not involve currency conversion and historically rely on correspondent banking networks, SWIFT messaging, and intermediary institutions — a chain that can introduce delays, costs, and opacity. Reducing the friction in cross-border payments has become a G20 priority, with initiatives focused on faster processing, lower costs, greater transparency, and broader access. Fintech competitors like Wise, Airwallex, and Thunes have captured significant market share by offering faster, cheaper alternatives to traditional correspondent banking channels.

Crowdfunding

Online platforms that connect individuals or companies seeking capital with a pool of potential investors or donors, bypassing traditional financial intermediaries. Crowdfunding takes several forms: reward-based (backers receive a product or perk), donation-based (charitable giving), loan-based (peer-to-peer lending), and equity-based (investors receive ownership stakes). Equity crowdfunding in particular has been enabled by regulatory frameworks that allow smaller investors to participate in early-stage company funding without the full regulatory burden of public securities offerings.

D

Data Localisation

Legal requirements that restrict the cross-border transfer of data, mandating that certain types of information — particularly financial data and personal data — be stored and processed within a specific country’s borders. Data localisation laws affect how financial institutions and fintech companies architect their cloud infrastructure, structure cross-border operations, and manage regulatory compliance across multiple jurisdictions. India, Russia, and China have among the most stringent data localisation requirements for financial data.

Decentralised Finance (DeFi)

An ecosystem of financial applications — lending, borrowing, trading, insurance, derivatives — built on blockchain networks using smart contracts, operating without traditional financial intermediaries. DeFi protocols execute financial transactions automatically based on coded rules, theoretically eliminating the need for banks, brokers, and exchanges. While DeFi has demonstrated the technical feasibility of programmable finance, it has also experienced significant security incidents (smart contract exploits, oracle manipulation) and remains largely outside the regulatory perimeter that governs traditional financial services. Regulators globally are working to bring DeFi activities within existing or new regulatory frameworks.

Deposit Insurance

A government-backed guarantee that protects depositors’ funds up to a specified limit if their bank fails. In the United States, the FDIC insures deposits up to $250,000 per depositor, per institution. In the EU, the Deposit Guarantee Scheme covers €100,000. Deposit insurance is a critical concept in the BaaS and neobank context — customers of fintech apps that hold funds through a partner bank are entitled to deposit insurance, but the pass-through mechanics (ensuring the FDIC recognises the end customer as the depositor rather than the fintech company) have been the subject of regulatory scrutiny following several BaaS-related failures in 2024–2025.

Digital Bank

A deposit-taking institution that delivers banking services primarily or exclusively through electronic channels — mobile apps, web interfaces, and APIs — rather than through physical branch networks. Digital banks hold their own licences and are regulated as banks, distinguishing them from neobanks (which typically operate under a partner bank’s licence) and challenger banks (a broader term). Several jurisdictions, including Singapore, Hong Kong, and Malaysia, have created specific digital bank licence categories with tailored capital and operational requirements.

Digital Identity (Digital ID)

A set of electronically captured attributes and credentials that uniquely identify an individual and can be verified digitally. Digital ID systems underpin modern KYC processes, enabling remote customer onboarding, e-signatures, and ongoing identity verification without physical document presentation. The EU’s eIDAS 2.0 regulation, expected to roll out through 2026–2027, will create a standardised European Digital Identity Wallet that citizens can use across borders for both public and private-sector services, including opening bank accounts and signing financial contracts.

Digital Payments

Any payment instruction initiated through electronic channels — computers, smartphones, point-of-sale terminals, or IoT devices — rather than through physical cash or cheques. Digital payments encompass card payments (credit and debit), bank transfers (credit and debit transfers), e-money transactions, mobile money, QR code payments, and account-to-account transfers initiated through open banking APIs. The shift to digital payments accelerated dramatically during the COVID-19 pandemic and has continued to expand, with real-time payment systems like FedNow and SEPA Instant making instant bank-to-bank transfers increasingly common.

Distributed Ledger Technology (DLT)

A broad category of technologies that maintain synchronised copies of a shared database across multiple locations, without relying on a single central authority. Blockchain is the most well-known form of DLT, but the category also includes directed acyclic graphs (DAGs), hashgraph, and other architectures. In financial services, DLT is being explored and deployed for trade settlement, securities tokenisation, cross-border payments, and supply chain finance — use cases where the ability to maintain a shared, tamper-evident record across multiple institutions offers clear advantages over centralised databases.

E

E-KYC (Electronic Know Your Customer)

Digital processes and technologies used to verify a customer’s identity remotely, without requiring in-person document presentation. E-KYC methods include document scanning and OCR (extracting data from photographed identity documents), biometric verification (matching a selfie to a document photograph using facial recognition), liveness detection (confirming the person is physically present rather than using a photograph), and database cross-referencing (checking identity details against government or credit bureau records). E-KYC has been transformative for financial inclusion — enabling account opening in minutes rather than days — but faces ongoing challenges around deepfake technology, synthetic identity fraud, and regulatory acceptance across jurisdictions.

Embedded Finance

The integration of financial products — payments, lending, insurance, accounts — directly into non-financial platforms and workflows. When a ride-hailing app processes your payment automatically, when an accounting platform offers invoice financing, or when an e-commerce checkout offers buy-now-pay-later — that is embedded finance. The enabling infrastructure typically involves a licensed bank (providing the regulatory framework), a BaaS or embedded finance platform (providing the API layer), and the non-financial company (providing the distribution and customer relationship). Embedded finance is projected to be a multi-trillion dollar market by 2030, though regulatory complexity and the recent scrutiny of BaaS arrangements have tempered growth expectations.

E-Money (Electronic Money)

A prepaid monetary value stored electronically — on a card, a mobile phone, or a server — that represents a claim on the issuer and is accepted as a means of payment by parties other than the issuer. E-money is distinct from bank deposits: it is typically issued by Electronic Money Institutions (EMIs) that hold a lighter licence than full banks and are subject to safeguarding requirements (keeping customer funds separate from operating capital) rather than prudential banking supervision. E-money includes prepaid cards, mobile money balances (like M-Pesa), and digital wallet balances held by payment platforms.

F

Factoring

A financing arrangement where a business sells its accounts receivable (unpaid invoices) to a third party (the factor) at a discount in exchange for immediate cash. The factor then collects payment directly from the business’s customers. Factoring provides working capital without adding debt to the balance sheet and is particularly valuable for SMEs with long payment cycles. Fintech platforms have digitised the factoring process, enabling faster invoice verification, automated credit assessment of debtors, and real-time funding decisions.

Fast Payments / Instant Payments

Payment systems that transmit payment messages and make funds available to the recipient in real time or near-real time, typically operating 24 hours a day, 365 days a year. In the United States, FedNow (launched July 2023) and The Clearing House’s RTP network provide instant payment capabilities. In Europe, SEPA Instant enables euro-denominated instant transfers across the Single Euro Payments Area. In India, UPI (Unified Payments Interface) processes billions of instant transactions monthly. Fast payment systems are fundamentally changing the economics of payment processing and enabling new use cases — from instant payroll and gig worker payments to request-to-pay and real-time account-to-account commerce.

Financial Action Task Force (FATF)

The inter-governmental body that establishes international standards for combating money laundering, terrorism financing, and proliferation financing. FATF Recommendations form the global baseline for AML/CFT regulation — and being placed on the FATF’s “grey list” (jurisdictions under increased monitoring) or “black list” (high-risk jurisdictions) has severe consequences for a country’s financial institutions, including increased compliance requirements from correspondent banks and potential loss of banking relationships. For fintech companies operating across borders, FATF standards effectively determine the minimum compliance framework they must meet in every jurisdiction.

FedNow

The Federal Reserve’s instant payment service, launched in July 2023, enabling financial institutions of all sizes to send and receive payments in seconds, around the clock, every day. Unlike the privately operated RTP network (run by The Clearing House), FedNow is operated by the central bank itself, giving it the broadest potential reach across the U.S. banking system. FedNow is particularly significant for community and regional banks, as it provides a path to offer instant payment capabilities without relying on larger bank intermediaries. Adoption has been gradual — as of 2026, thousands of institutions have enrolled but usage volumes are still ramping up relative to existing ACH and wire transfer volumes.

Fintech (Financial Technology)

Technology-driven innovation that transforms how financial services are designed, delivered, and consumed. The term encompasses a vast range of applications — from mobile payments and peer-to-peer lending to AI-powered credit decisioning, robotic process automation in compliance, and blockchain-based settlement systems. As a category, fintech now includes both standalone technology companies (like Stripe, Plaid, and Revolut) and the internal innovation efforts of established financial institutions. The global fintech market was valued at over $116 billion in investment during 2025, though the sector has matured beyond its growth-at-all-costs phase into one where profitability, regulatory sustainability, and infrastructure resilience increasingly determine which companies succeed.

G

Gazelle Company

A high-growth company that sustains at least 20 percent annual revenue growth over a multi-year period. Gazelles are disproportionately important for job creation and economic growth. In the fintech context, gazelle-stage companies are typically those that have found product-market fit and are scaling rapidly — the phase where banking infrastructure, payment processing capacity, and compliance frameworks are tested most severely. Identifying and serving gazelle companies is a strategic focus for BaaS providers and commercial banks alike.

H

Hyper Banking (High-Performance Banking)

An approach to banking that prioritises real-time data processing, advanced analytics, and seamless customer experiences delivered through cutting-edge technology infrastructure. Hyper banking goes beyond digital banking (which is primarily about channel delivery) to encompass the entire operational architecture — real-time risk assessment, dynamic pricing, predictive service delivery, and infrastructure that can scale instantly to meet demand spikes. The concept is aspirational for many institutions but reflects the direction of travel as cloud-native core banking and AI-driven operations become the competitive baseline.

I

Interoperability

The ability of different payment systems, platforms, or technical standards to work together seamlessly — enabling a customer of one payment provider to transact with a customer of another as if they were on the same system. Interoperability is the holy grail of payment infrastructure: without it, every payment network becomes a walled garden. Achieving interoperability requires alignment across technical standards (data formats, messaging protocols), legal frameworks (mutual recognition of licences and consumer protections), and commercial agreements (interchange pricing, settlement rules). The ISO 20022 messaging standard, now being adopted globally, is designed to enhance interoperability across domestic and cross-border payment systems.

Internet of Things (IoT)

A network of physical devices embedded with sensors, software, and connectivity that enables them to collect and exchange data. In financial services, IoT applications include tracking shipped goods for trade finance collateral monitoring, enabling usage-based insurance pricing through vehicle telematics, triggering automatic payments when sensor-equipped equipment completes a delivery, and monitoring asset condition for equipment-backed lending. IoT data feeds are increasingly integrated into AI-driven financial models, providing real-time signals that improve the accuracy of credit, insurance, and risk assessment decisions.

ISO 20022

A global standard for financial messaging that provides a common language for electronic communications between financial institutions. ISO 20022 uses a richer, more structured data format than legacy messaging standards (like SWIFT MT messages), enabling more detailed transaction information to travel with each payment. This additional data supports better compliance screening, automated reconciliation, and enhanced analytics. SWIFT’s cross-border payments network completed its migration to ISO 20022 for high-value payments, and domestic payment systems worldwide are in various stages of adoption. For banks, the ISO 20022 migration is one of the most significant infrastructure projects of the current decade.

K

KYC (Know Your Customer)

The mandatory process through which financial institutions verify the identity of their customers, understand the nature of their activities, assess their risk profile, and conduct ongoing monitoring to ensure that transactions are consistent with what is known about the customer. KYC is a foundational component of AML/CFT compliance and is required before any financial institution can open an account or establish a business relationship. The process encompasses Customer Due Diligence (CDD), Enhanced Due Diligence (EDD) for higher-risk customers, and ongoing transaction monitoring. AI and regtech solutions are transforming KYC from a manual, document-heavy process into an automated, data-driven workflow — though the regulatory obligation and ultimate responsibility remain with the financial institution.

M

Machine Learning (ML)

A subset of artificial intelligence in which algorithms improve their performance on a task through exposure to data, without being explicitly programmed for each scenario. In financial services, ML models are deployed for credit risk assessment (learning from historical loan performance to predict future defaults), fraud detection (identifying transaction patterns associated with fraudulent activity), customer segmentation, pricing optimisation, and regulatory compliance automation. The regulatory framework for ML in banking — particularly around model validation, explainability, and bias testing — is a rapidly evolving area that compliance teams must navigate carefully.

Mobile Money

An e-money product where the balance is stored electronically and can be accessed, transferred, or spent through mobile phone-based payment instructions. Mobile money has been transformative for financial inclusion, particularly in Sub-Saharan Africa and South Asia, where M-Pesa (Kenya), bKash (Bangladesh), and GCash (Philippines) have provided basic financial services to hundreds of millions of people who lack traditional bank accounts. Mobile money systems rely on agent networks (cash-in, cash-out points, typically small retailers) and are increasingly interoperable with bank accounts and international payment networks.

N

Neobank

A technology company that provides banking-like services — accounts, cards, payments, sometimes lending — without holding its own full banking licence. Neobanks typically operate through a BaaS arrangement with a licensed partner bank that provides the regulatory framework and deposit insurance coverage. The distinction between neobanks (no licence of their own) and digital banks (licensed deposit-taking institutions) is critical from a regulatory and consumer protection perspective, though the terms are often used interchangeably in popular coverage. Notable neobanks include Chime (US), N26 (EU), and Nubank (Brazil, though Nubank subsequently obtained its own banking licence).

Near-Field Communication (NFC)

A short-range wireless technology that enables communication between devices when they are brought within a few centimetres of each other. NFC is the technology behind contactless card payments (tap-to-pay), mobile wallet transactions (Apple Pay, Google Pay), and some transit ticketing systems. The EU’s recently adopted regulation requiring Apple to open NFC access on iPhones to third-party payment providers is a landmark decision that illustrates how NFC’s role in payment infrastructure has become a matter of competition policy.

O

Open Banking

A regulatory and technology framework that enables consumers and businesses to share their bank account data and payment capabilities with authorised third-party providers through secure APIs, with the customer’s explicit consent. Open banking was mandated in Europe through PSD2, implemented in the UK through the Open Banking Implementation Entity, and is being pursued globally through varying approaches — from Australia’s Consumer Data Right to India’s Account Aggregator framework. The evolution from open banking (covering payment accounts) to open finance (extending to investments, insurance, pensions, and mortgages) is now underway, with the EU’s proposed Financial Data Access (FIDA) regulation and the transition from PSD2 to PSD3/PSR representing the next regulatory chapter. Open banking has enabled entirely new categories of financial services — personal finance management, account-based lending, automated reconciliation, and payment initiation services that bypass traditional card networks.

P

Peer-to-Peer Lending (P2P)

Online platforms that connect individual or institutional lenders directly with borrowers, facilitating loans without routing through a traditional bank. P2P lending platforms handle origination, credit assessment, servicing, and collections, earning revenue through origination fees and/or spread. The sector has matured significantly since its early years — many P2P platforms have obtained lending licences, shifted toward institutional funding sources, and evolved into more traditional marketplace lending businesses. Regulatory requirements have tightened across jurisdictions, with the EU’s European Crowdfunding Service Providers Regulation (ECSPR) establishing a harmonised framework for platforms operating across member states.

Payment Service Provider (PSP)

Any entity that provides payment services to end users — a broad category that includes banks, e-money institutions, payment aggregators, payment gateways, money transfer operators, and card acquirers. In the European regulatory framework, PSPs are classified as Account Servicing Payment Service Providers (ASPSPs, typically banks), Account Information Service Providers (AISPs), and Payment Initiation Service Providers (PISPs). The PSP landscape has become increasingly complex as non-bank payment providers — companies like Stripe, Adyen, and Square — have grown to process volumes that rival or exceed many traditional bank payment operations.

R

RegTech (Regulatory Technology)

Technology solutions designed to help financial institutions meet regulatory and compliance requirements more efficiently, accurately, and at lower cost. RegTech applications span the full compliance lifecycle: identity verification and KYC, transaction monitoring and suspicious activity detection, regulatory reporting automation, sanctions screening, regulatory change management, and model risk management. As compliance costs have risen — consuming 10–15 percent of operating budgets at many banks — regtech has shifted from a nice-to-have to a strategic necessity. The most advanced regtech solutions use AI and machine learning to reduce false positive rates in transaction monitoring, automate regulatory filing preparation, and keep pace with the growing volume of regulatory change across jurisdictions.

Real-Time Gross Settlement (RTGS)

A payment settlement system in which each transaction is processed individually and settled in real time with finality, rather than being batched and netted against other transactions. RTGS systems are operated by central banks and are used for high-value, time-critical payments — interbank transfers, securities settlement, and large commercial transactions. Fedwire (US), TARGET2 (Eurozone), and CHAPS (UK) are examples of RTGS systems. The key distinction from retail fast payment systems (like FedNow) is that RTGS settles in central bank money with immediate finality, making it the highest-assurance payment mechanism available.

Robo-Advisory

Automated, algorithm-driven investment management services that build and rebalance portfolios based on a client’s risk tolerance, financial goals, and time horizon — typically at a fraction of the cost of traditional human financial advisors. Platforms like Wealthfront, Betterment, and Nutmeg pioneered the category, and most major wealth management firms now offer robo-advisory capabilities alongside their human advisor services. The next generation of robo-advisory is incorporating AI-powered financial planning that goes beyond portfolio allocation to encompass tax optimisation, retirement income planning, and holistic financial wellness.

S

Sandbox / Regulatory Sandbox

A controlled, time-limited environment in which fintech companies and other innovators can test new products, technologies, or business models under regulatory supervision — often with temporary waivers or modified compliance requirements. Sandboxes are designed to encourage innovation while protecting consumers and maintaining financial stability. The UK’s Financial Conduct Authority pioneered the concept in 2016, and over 50 jurisdictions now operate some form of innovation facilitator. The effectiveness of sandboxes remains debated: supporters argue they accelerate innovation and improve regulatory understanding, while critics contend that few sandbox participants have successfully scaled beyond the sandbox environment.

SaaS (Software-as-a-Service)

A software delivery model where applications are hosted centrally and accessed by users over the internet on a subscription basis, rather than being installed and maintained on local infrastructure. In financial services, SaaS has become the dominant delivery model for everything from loan origination systems and compliance platforms to treasury management, data analytics, and customer engagement tools. The SaaS model shifts technology spending from capital expenditure to operational expenditure, enables continuous updates without disruptive upgrade cycles, and allows financial institutions to adopt best-of-breed solutions from specialist vendors rather than relying on monolithic systems from a single provider.

Stablecoin

A type of cryptocurrency designed to maintain a stable value relative to a reference asset — typically a fiat currency like the U.S. dollar. Stablecoins achieve this peg through various mechanisms: fiat-backed (holding reserves of the reference currency, like USDT and USDC), crypto-collateralised (over-collateralised with other crypto assets), or algorithmic (using supply-and-demand mechanisms to maintain the peg). Stablecoins have become a critical component of crypto market infrastructure and are increasingly used for cross-border payments, remittances, and settlement. Regulatory frameworks specifically addressing stablecoins — including the EU’s MiCA regulation and proposed U.S. legislation — are being implemented to address risks around reserve adequacy, consumer protection, and systemic stability.

SupTech (Supervisory Technology)

Technology solutions used by financial regulators and supervisory authorities to enhance the efficiency and effectiveness of their oversight activities. SupTech mirrors regtech but from the regulator’s side — using AI and data analytics to monitor financial institutions’ compliance, detect emerging systemic risks, process regulatory filings, and identify patterns across the financial system that individual institution-level reporting might miss. The adoption of suptech by regulators has implications for supervised institutions, as it means that regulatory scrutiny is becoming more data-driven, automated, and potentially more granular than the periodic examination model of the past.

Super App

A mobile application that combines multiple services — messaging, social media, ride-hailing, food delivery, financial services — into a single platform, aiming to become the primary digital interface for a user’s daily life. WeChat (China), Grab (Southeast Asia), and Gojek (Indonesia) are the defining examples. Financial services — payments, lending, insurance, investments — are a core component of the super app model because they generate transaction data, enable monetisation, and create switching costs. Western markets have been slower to develop super apps due to different competitive dynamics, privacy regulations, and consumer preferences, though companies like Revolut and PayPal have pursued super app-like strategies.

T

Tokenisation

A term with three distinct meanings in financial services, and confusing them leads to misunderstandings. First, asset tokenisation: representing ownership of real-world assets (securities, real estate, commodities) as digital tokens on a blockchain, enabling fractional ownership and potentially more efficient transfer and settlement. Second, payment tokenisation: replacing sensitive card numbers with unique substitute identifiers (tokens) during transaction processing, so that the actual card number is never exposed to merchants or intermediaries — a critical security measure for digital commerce. Third, data tokenisation: replacing sensitive data with non-sensitive placeholders for storage and processing, used widely in compliance and data protection contexts. When someone in fintech says “tokenisation,” the context determines which of these three meanings applies.

V

Venture Capital (VC)

Equity investment in early-stage and high-growth companies, provided by professional fund managers in exchange for ownership stakes. VC has been the primary funding mechanism for the fintech industry, with global fintech VC investment peaking in 2021 before declining through 2023 and showing signs of recovery in 2025. VC-backed fintech companies typically pursue rapid growth with the expectation of achieving a large-scale exit (IPO or acquisition) that generates returns for investors. The shift in VC sentiment toward profitability over growth-at-all-costs has had profound implications for fintech business models, pricing strategies, and competitive dynamics.

Virtual IBAN (vIBAN)

A virtual account number that functions like a traditional International Bank Account Number but is not tied to a physical bank account in the traditional sense. vIBANs enable businesses to assign unique identifiers to individual clients, transactions, or business lines — simplifying reconciliation, enabling multi-currency operations, and providing greater control over payment flows. Typically issued by fintech companies and EMIs, vIBANs have become a standard tool for B2B payment management, marketplace platforms, and cross-border treasury operations. Regulatory attention to vIBANs has increased as supervisors seek clarity on the pass-through deposit insurance implications and the AML obligations associated with virtual account structures.

Virtual Asset Service Provider (VASP)

An entity that conducts virtual asset-related business on behalf of others — exchanging virtual assets for fiat currency or other virtual assets, transferring virtual assets between parties, providing custody or administration of virtual assets, or offering financial services related to virtual asset issuance and sales. The VASP classification, defined by the FATF, brings crypto businesses within the scope of AML/CFT regulation. Under the EU’s MiCA regulation, VASPs operating in Europe must obtain authorisation as Crypto-Asset Service Providers (CASPs) and comply with comprehensive prudential, conduct, and governance requirements — a significant step toward regulatory parity with traditional financial services.

60+

Terms Defined

Alphabetical Sections

2026

Updated For

A–V

Coverage Span

Frequently Asked Questions

Fintech Terminology — Common Questions

What is the difference between a neobank, a digital bank, and a challenger bank?

These three terms are frequently confused but describe different types of institutions. A neobank is a technology company that offers banking-like services without its own banking licence — it operates through a licensed partner bank. A digital bank holds its own full banking licence and delivers services primarily through digital channels rather than branches. A challenger bank is a newly licensed bank that competes against established incumbents — it holds its own licence (like a digital bank) but the term emphasises its competitive positioning rather than its delivery channel. The regulatory implications are significant: neobank customers depend on the partner bank’s licence and deposit insurance, while digital bank and challenger bank customers have a direct regulatory relationship with the institution.

How does open banking differ from Banking-as-a-Service (BaaS)?

Open banking is primarily about data access and payment initiation — it enables authorised third parties to read account information and initiate payments from a customer’s existing bank account, with the customer’s consent, through standardised APIs. BaaS goes much further: it allows non-bank companies to embed actual banking products — accounts, cards, loans — into their own platforms by connecting to a licensed bank’s infrastructure. Open banking lets you see your bank data in a budgeting app; BaaS lets a ride-hailing company offer you a bank account. They share API-first architecture and often use the same infrastructure providers, but they serve different purposes and are governed by different regulatory frameworks.

What is the difference between regtech and suptech?

Regtech and suptech are two sides of the same coin. Regtech (regulatory technology) is used by financial institutions to meet their compliance obligations more efficiently — automating KYC, transaction monitoring, regulatory reporting, and sanctions screening. Suptech (supervisory technology) is used by regulators themselves to enhance their oversight capabilities — analysing data from supervised institutions, detecting systemic risks, and monitoring compliance across the financial system. The practical implication is that as regulators adopt suptech tools, they can scrutinise institutions with greater granularity and speed than traditional examination approaches allowed — which in turn drives demand for more sophisticated regtech solutions within the institutions being supervised.

What is FedNow and how is it different from ACH?

Both are payment systems operated within the U.S. financial infrastructure, but they work fundamentally differently. ACH (Automated Clearing House) processes transactions in batches — payments are collected throughout the day and settled in groups, which means funds typically take one to two business days to arrive (same-day ACH is available but still involves batch processing). FedNow, launched by the Federal Reserve in July 2023, settles each transaction individually in real time, around the clock, including weekends and holidays. FedNow is designed for the instant economy — payroll, bill payments, and person-to-person transfers that need to arrive in seconds rather than days. ACH remains dominant by volume due to its lower cost and established infrastructure, but FedNow adoption is growing as more institutions connect to the service.

What does tokenisation mean in finance — and why are there three different definitions?

Tokenisation is one of the most overloaded terms in financial technology, and using the wrong definition leads to confusion. Asset tokenisation means representing ownership of real-world assets (shares, property, commodities) as digital tokens on a blockchain, enabling fractional ownership and programmable transfer. Payment tokenisation is a security technique that replaces sensitive card numbers with substitute identifiers during transactions, so the real card number is never exposed. Data tokenisation replaces sensitive information with non-sensitive placeholders for storage and compliance purposes. When you encounter the word in a conversation or article, the context will tell you which meaning applies — but it is worth asking for clarification if the context is ambiguous, because the three concepts have very different technical and regulatory implications.

Why does KYC matter for fintech companies if they are not banks?

KYC obligations extend well beyond traditional banks. Any entity that provides financial services — payment processors, e-money issuers, lending platforms, crypto exchanges, money transfer operators — is subject to KYC requirements under AML/CFT regulations. Even fintech companies that operate through a BaaS partnership with a licensed bank typically perform customer-facing KYC on behalf of their partner bank, though the regulatory responsibility ultimately rests with the licence holder. The FATF’s standards apply to all “financial institutions” and “designated non-financial businesses and professions” — a broad classification that captures most fintech business models. Failure to implement adequate KYC processes can result in regulatory enforcement, loss of banking relationships, and criminal liability.

What is the difference between a stablecoin and a CBDC?

Both are digital representations of value designed to maintain a stable price, but they differ fundamentally in who issues them and what backs them. A CBDC is issued directly by a central bank — it is sovereign money in digital form, carrying the same backing and trust as physical currency. A stablecoin is issued by a private company and maintains its peg to a reference currency through reserve assets (fiat deposits, government bonds) or algorithmic mechanisms. The distinction matters for consumer protection (CBDC users have a direct claim on the central bank; stablecoin users have a claim on a private issuer), monetary policy (CBDCs could give central banks new policy tools), and systemic risk (a stablecoin issuer can fail; a central bank in its own currency cannot).

How is AI changing compliance in financial services?

AI is transforming compliance from a largely manual, rules-based function into an increasingly automated, data-driven capability. In transaction monitoring, machine learning models can reduce false positive rates by 50–70 percent compared to traditional rules-based systems — a significant efficiency gain given that AML analysts spend most of their time investigating alerts that turn out to be legitimate activity. In KYC, AI-powered document verification, facial recognition, and natural language processing are enabling remote onboarding in minutes rather than days. In regulatory reporting, AI can automate the extraction of required data points from internal systems and format them according to regulatory specifications. However, the deployment of AI in compliance also creates new challenges: model risk management (ensuring AI systems behave as expected), explainability (demonstrating to regulators why a particular decision was made), and bias prevention (ensuring AI does not discriminate against protected groups). The EU AI Act classifies AI used in credit scoring and financial services as “high-risk,” subject to specific governance, transparency, and oversight requirements.

Virginia Heritage is an independent publication. This glossary represents our editorial understanding of commonly used fintech and financial services terminology as of 2026. Definitions are provided for educational purposes and should not be construed as legal, regulatory, or financial advice. Regulatory requirements vary by jurisdiction.

The post The Ultimate Fintech Glossary 2026: 60+ Terms Every Banking and Financial Technology Professional Needs to Know appeared first on Virginia Heritage.

Global Fintech Investment Hits $116 Billion in 2025: What the Data Means for Financial Institutions

Virginia Heritage — Sat, 04 Apr 2026 10:45:22 +0000

Fintech Investment · Industry Analysis · 2025

After three consecutive years of declining investment, the global fintech market turned a corner in 2025. Total investment rose from $95.5 billion to $116 billion year-over-year — driven by growing deal sizes, surging interest in digital assets and AI, and a reopening exit environment that more than doubled in value. For community banks, regional lenders, and financial services firms watching these shifts from the outside, the data offers a clear signal: fintech infrastructure is no longer speculative. It is becoming the operating layer of financial services.

This analysis draws on the latest Pulse of Fintech report, covering over 4,700 deals across venture capital, private equity, and M&A activity globally. Here is what the numbers tell us about where fintech is heading — and what it means for traditional financial institutions.

2025 at a Glance

$116B total global fintech investment

4,719 deals (8-year low in volume)

$19.1B invested in digital assets (nearly doubled)

$16.8B invested in AI-focused fintechs

$104.4B in exit value (3rd highest ever)

Global Fintech Investment: Four-Year Trend

Year	Total Investment	Deal Count	VC	M&A	PE Growth
2022	$168.4B	8,314	$92.1B	$65.6B	$10.7B
2023	$119.3B	5,764	$51.1B	$58.6B	$9.6B
2024	$95.5B	5,533	$45.4B	$44.6B	$5.5B
2025	$116.0B	4,719	$56.7B	$55.3B	$4.0B

The paradox of 2025: investment rose 21 percent year-over-year while deal volume fell to an eight-year low. Investors are deploying more capital into fewer, larger deals — focusing on proven companies with scalable business models rather than spreading bets across early-stage ventures. The median M&A deal size jumped from $36.9 million in 2024 to $59.9 million in 2025. Venture growth-stage pre-money valuations surged from $168.3 million to $977.5 million. This is a market that rewards scale and punishes fragmentation.

Where the Money Is Going: Regional and Sector Breakdown

Investment by Region (2025)

Region	Full Year 2025	H2 2025	Deal Count (FY)	YoY Change
Americas	$66.5B	$27.4B	2,409	+20% (from $55.4B)
EMEA	$29.2B	$13.8B	1,484	+10% (from $26.5B)
ASPAC	$9.3B	$4.6B	763	-21% (from $11.7B)

The Americas dominated with 57 percent of global fintech investment, led by the US at $56.6 billion. The EMEA region showed resilience, with the UK attracting $10.9 billion and Sweden punching well above its weight at $4.8 billion. Asia-Pacific was the only region to see a year-over-year decline, driven by economic headwinds in China and continued caution from investors in Australia.

Investment by Fintech Segment (2025)

Segment	2024	2025	Deal Count	Trend
Payments	$20.4B	$19.2B	542	Flat — capital concentrating on proven platforms
Digital Assets	$11.2B	$19.1B	1,199	Nearly doubled — stablecoins, tokenization, IPOs
AI-focused Fintech	$12.1B	$16.8B	1,334	Rising — corporates driving efficiency adoption
Insurtech	$2.9B	$8.6B	291	Bounced back — driven by two outlier M&A deals
Regtech	$6.8B	$4.9B	519	Declining — corporates building AI internally
Wealthtech	$4.9B	$1.4B	57	Sharp drop from record 2024 high
Cybersecurity	$0.9B	$0.7B	72	7-year low — consolidation around platform players

Digital assets was the breakout story of 2025. Investment nearly doubled to $19.1 billion, driven by regulatory clarity — the GENIUS Act in the US and MiCA enforcement in the EU — stablecoin infrastructure buildouts by major banking consortiums, and a wave of digital-asset-focused IPOs. AI-focused fintechs attracted $16.8 billion, although a significant share of AI investment went to big tech partnerships rather than standalone fintech startups. The payments sector remained flat as investors consolidated around proven platforms and shifted attention to emerging market infrastructure in South America, Africa, and Southeast Asia.

The Exit Market Reopens: IPOs, M&A, and What It Signals

After years of stagnation, the fintech exit market came back to life in 2025. Global exit value more than doubled from $46.8 billion to $104.4 billion — the third highest level ever recorded. VC-backed fintech IPOs accounted for $63 billion globally, the second-highest annual total after the 2021 boom. Post-IPO performance has been reasonable, suggesting the market is rewarding real business fundamentals rather than narrative.

Largest Global Fintech Deals — H2 2025

Deal Size	Sector	Type	Region
$3.0B	Banking / Financial services	Late-stage VC	UK
$2.5B	Insurtech / SaaS	Take-private	Israel
$2.0B	Prediction markets	Late-stage VC	US
$2.0B	Lending	Take-private	US
$1.0B	Prediction markets	Late-stage VC	US
$1.0B	Banking / Financial services	Late-stage VC	UK
$1.0B	B2B / Treasury management	M&A	US
$895M	Investment infrastructure	M&A	UK
$835M	Trading / Back-office	Secondary buyout	US
$820M	Wealthtech	PE growth	US

The pattern in the top deals is instructive: late-stage VC dominates, take-privates signal PE confidence in mature platforms, and the UK punched well above its weight with three of the top ten deals. Notably absent from the top ten are early-stage consumer fintechs — investors are backing infrastructure, B2B platforms, and companies with proven revenue, not consumer-facing experiments.

What This Means for Traditional Financial Institutions

The investment data tells a story that goes beyond venture capital. Three trends matter directly for banks and credit unions: the shift of B2B payments infrastructure from niche to mainstream, the rapid maturation of digital assets from speculative to institutional, and the growing dominance of AI in compliance, risk, and operational efficiency.

In payments, the move toward real-time infrastructure, ISO 20022 adoption, and multi-rail orchestration is creating opportunities for institutions willing to modernise their payment rails — and existential pressure on those that are not. In digital assets, major banking consortiums are already building stablecoin infrastructure and tokenizing money market funds. And in AI, larger institutions are increasingly building compliance and risk tools internally rather than relying on third-party regtech providers — a shift that is reshaping the vendor landscape.

For community and regional banks, the takeaway is not that they need to compete with billion-dollar fintechs. It is that the infrastructure these fintechs are building — faster payments, better data access, more efficient compliance — will become the baseline that customers expect from every financial institution. The gap between what fintech-native customers experience elsewhere and what traditional institutions offer is the gap that determines competitiveness in 2026 and beyond.

Frequently Asked Questions

Fintech Investment Trends 2025

How much was invested in fintech globally in 2025?

Total global fintech investment reached $116 billion in 2025 across 4,719 deals, up from $95.5 billion in 2024. This marked the first year-over-year increase after three consecutive years of decline from the 2021 peak of $238.9 billion. Venture capital accounted for $56.7 billion, M&A activity contributed $55.3 billion, and PE growth funding added $4 billion. The Americas led with $66.5 billion, followed by EMEA at $29.2 billion and ASPAC at $9.3 billion.

Why did fintech deal volume fall to an eight-year low despite investment rising?

The decline in deal volume alongside rising investment reflects a fundamental shift in investor behaviour: capital is concentrating on fewer, larger deals. Investors are increasingly selective, focusing on late-stage companies with proven business models and clear paths to profitability or exit. The median M&A deal size jumped from $36.9 million to $59.9 million in 2025, and venture growth pre-money valuations surged to $977.5 million. Early-stage companies without differentiated offerings or clear revenue models are finding it significantly harder to raise capital.

What drove the surge in digital assets investment in 2025?

Three factors converged to drive digital assets investment from $11.2 billion to $19.1 billion in 2025. First, regulatory clarity — the GENIUS Act in the US provided a framework for stablecoins, while the EU’s MiCA regulation came into full force. Second, institutional participation accelerated, with major banking consortiums announcing plans to issue stablecoins and asset managers tokenizing money market funds and ETFs. Third, the IPO market reopened for digital-asset companies, with successful listings raising hundreds of millions. The combination of regulatory certainty, institutional adoption, and viable exit paths created a self-reinforcing cycle of confidence and capital deployment.

How is AI reshaping the fintech investment landscape?

AI-focused fintechs attracted $16.8 billion in investment across 1,334 deals in 2025 — up from $12.1 billion the year before. However, the AI story in fintech is nuanced. Corporates, particularly large banks and financial institutions, are increasingly partnering directly with big tech and AI firms rather than investing in standalone AI fintechs. Many institutions are building compliance, risk management, and cybersecurity tools internally using AI. This is compressing the addressable market for third-party AI fintech providers and pushing them to develop genuinely differentiated intellectual property or risk being bypassed entirely.

What are the key fintech trends to watch in 2026?

Five trends are expected to define fintech in 2026: continued momentum in stablecoins and real-world asset tokenization, driven by regulatory frameworks now in place across the US and EU; growing disruption in capital markets as startups target equity, debt, and private credit infrastructure; increasing fintech IPO activity as mature companies exit; asset management transformation, particularly in Asia-Pacific; and the emergence of agentic AI in payments, compliance, and cybersecurity. The overarching theme is maturation — fintech is moving from experimentation to infrastructure, from narrative-driven fundraising to revenue-driven valuation.

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Xero’s Anthropic Partnership Is a Signal That AI Financial Assistants Are Coming for the Back Office

Virginia Heritage — Fri, 27 Mar 2026 19:37:53 +0000

The announcement that Xero has signed a multi-year partnership with Anthropic to embed Claude directly into its small business accounting platform is worth paying attention to — not because AI partnerships are novel at this point, but because of what this particular integration reveals about where financial software is heading.

Xero is not bolting a chatbot onto a sidebar. The integration targets what the company calls agentic workflows — systems that do not simply answer questions about your financial data but actively monitor it, identify problems, and recommend or execute actions without being asked. Their AI layer, branded JAX (Just Ask Xero), is being rebuilt around Claude’s reasoning capabilities to handle tasks like cash flow prediction, overdue invoice identification, and scenario modelling that previously required either a human bookkeeper’s judgment or a dedicated financial analyst.

For the millions of small businesses that run on Xero globally, the practical implication is significant. The questions that consume hours of owner time every month — why is cash tight, which invoices are overdue, can I afford to hire — are exactly the kind of structured financial reasoning that large language models handle well when connected to clean, real-time data. And accounting platforms have the cleanest, most structured financial data of any software category.

What the integration actually includes

The partnership has two distinct components that are worth separating, because they serve different users and reflect different levels of ambition.

Component	What It Does	Who Benefits
JAX inside Xero	AI agent monitors financial data in real time, predicts cash flow gaps, flags overdue invoices, suggests actions — moves from passive reporting to proactive financial management	Small business owners and their accountants/bookkeepers working within Xero daily
Xero data inside Claude.ai	Users bring live Xero financial data into Claude for detailed business planning, scenario modelling, and year-end analysis combining internal metrics with external market context	Business owners and financial advisors who want deeper analytical capabilities beyond standard accounting workflows
Internal engineering adoption	Xero’s own product teams adopt Claude and Anthropic’s Cowork tools to accelerate development of new platform features	Xero’s engineering organisation — with downstream benefits for all platform users through faster feature delivery

The first component is evolutionary — a smarter assistant inside existing accounting software. The second is more interesting. Allowing users to pull live financial data into a general-purpose AI reasoning environment means that the analytical capabilities available to a small business owner are no longer constrained by what the accounting software vendor chose to build into their reporting module. A bakery owner could ask Claude to model the financial impact of opening a second location using their actual revenue data, supplier costs, and cash flow patterns — the kind of analysis that would previously have required hiring a consultant or building a spreadsheet model from scratch.

Why this matters beyond Xero

The broader signal here is about the collapsing boundary between accounting software and financial intelligence. For decades, these have been separate categories. Accounting platforms recorded transactions. Financial intelligence — the interpretation of those transactions into actionable business strategy — was the domain of human advisors, analysts, and consultants.

AI connected to real-time financial data dissolves that boundary. When a system can read your profit and loss statement, cross-reference it with your outstanding receivables, factor in seasonal patterns from your historical data, and then tell you that you will have a cash shortfall in six weeks unless you collect on three specific invoices — that is not accounting software anymore. That is a financial advisor that works around the clock and costs nothing beyond the platform subscription.

The implications for the accounting profession are considerable. Bookkeeping and basic financial reporting — the tasks that constitute the majority of billable hours for small-firm accountants — are precisely the tasks that agentic AI handles most effectively. The accountants and advisors who thrive in this environment will be those who move upstream into strategic advisory work that requires judgment, relationship context, and business understanding that AI cannot replicate. The ones who continue to compete on data entry and reconciliation speed will find their value proposition increasingly difficult to sustain.

The data security question

Both companies have stated that financial data shared during sessions is used exclusively for that specific interaction and is not used to train Anthropic’s models. This is the correct approach, and it is also the minimum viable commitment for any AI integration that touches financial data. The real test will be in the implementation detail — how session data is handled, where it is processed, how long it persists, and whether the security architecture holds up under regulatory scrutiny from financial authorities who are increasingly focused on AI data governance.

For community banks and regional financial institutions watching this space, the Xero-Anthropic partnership is a preview of what is coming across the entire financial services stack. If a cloud accounting platform can offer AI-powered cash flow prediction and scenario modelling to a sole proprietor, the expectation gap between what small business customers experience from their accounting software and what they experience from their bank will only widen. Institutions that cannot match that level of financial intelligence in their own digital channels will feel the competitive pressure.

What comes next

The integration is expected to roll out over the coming months. The trajectory is clear — Xero is positioning itself not as an accounting platform that happens to have AI features, but as an AI-powered financial operating system where accounting is one function among many. Whether that ambition is achievable depends on execution, but the strategic direction is unambiguous.

For the financial technology sector more broadly, this partnership confirms that the major AI model providers are now competing aggressively for embedded positions within vertical software platforms. Anthropic’s move into fintech through Xero mirrors similar plays across healthcare, legal, and enterprise software. The model providers that win distribution through the platforms where professionals already work will capture significantly more value than those selling standalone AI tools that require users to change their workflows.

The interesting question is not whether AI will transform small business financial management — that outcome is now inevitable. The question is how quickly the institutional financial services sector adapts its own offerings in response, and whether community banks and credit unions can deploy similar capabilities before the gap between fintech platforms and traditional banking becomes irreversible.

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Klarna’s Integration with Stripe’s Shared Payment Tokens Reveals the Real Infrastructure Problem Behind Agentic Commerce

Virginia Heritage — Mon, 10 Feb 2025 19:40:52 +0000

There is an infrastructure problem at the heart of agentic commerce that most coverage of AI shopping assistants has ignored. When an autonomous AI agent purchases something on behalf of a consumer, it defaults to a stored card — a credit or debit card already on file. That is the only payment method most agent architectures can handle. Every alternative payment option — buy now pay later, bank transfers, digital wallets, instalment plans — gets silently excluded from the transaction.

Klarna and Stripe have moved to close that gap. Klarna’s flexible payment options will now be supported within AI agent-driven checkout flows through Stripe’s Shared Payment Tokens (SPTs), a mechanism designed to let autonomous systems initiate purchases using a customer’s preferred payment method without the agent ever accessing the underlying payment credentials.

For US merchants already running Klarna through Stripe, no additional integration is required. The capability layers into existing infrastructure. But the significance extends well beyond a single payment provider adding support for a new transaction channel.

What Shared Payment Tokens actually solve

The technical constraint is straightforward but consequential. AI shopping agents operate on behalf of a consumer but are not the consumer. They cannot authenticate through a bank’s 3D Secure flow, cannot complete a BNPL credit check, and cannot interact with payment interfaces designed for human users. Standard card-on-file transactions work because the token is pre-authorised and requires no interactive steps. Everything else breaks.

Stripe’s SPTs create a permissioned layer between the consumer’s actual payment methods and the agent executing the purchase. The agent can reference a specific payment option — including Klarna’s interest-free credit — without seeing card numbers, bank details, or credit terms. The authorisation happens through the token infrastructure rather than through user-facing authentication flows.

Payment Scenario	Without SPTs	With SPTs
Card-on-file purchase	Works — agent uses stored card token directly	Works — no change to existing flow
Buy now, pay later (Klarna)	Blocked — agent cannot complete interactive credit check or redirect flow	Supported — agent references Klarna token, authorisation handled through permissioned layer
Bank transfer / open banking	Blocked — requires bank authentication the agent cannot perform	Future potential — SPT architecture extensible to additional payment methods
Consumer payment preference	Overridden — agent defaults to whatever card is stored regardless of user preference	Preserved — agent can select from consumer’s full range of authorised payment methods

This is a meaningful shift. When AI agents can only use stored cards, consumers lose the financial flexibility they have come to expect from modern checkout experiences. For a consumer who routinely uses interest-free instalments for larger purchases, having an AI agent bypass that option and charge the full amount to a debit card is not just inconvenient — it changes the economics of the transaction from the buyer’s perspective.

Why this matters for payment infrastructure

The deeper issue is about who controls the checkout experience in a world where purchasing decisions are increasingly delegated to software. Today, the number of transactions executed by autonomous agents is small. Within a few years, as AI assistants from Google, Apple, Amazon, and standalone startups mature, a significant share of e-commerce volume will flow through automated purchasing systems.

If the infrastructure powering those systems only supports card-on-file transactions, then every alternative payment method — BNPL, real-time bank payments, digital wallets, instalment plans — risks being structurally excluded from the fastest-growing transaction channel in e-commerce. That is an existential threat for companies like Klarna whose entire business model depends on being present at the point of purchase.

Stripe’s position in this shift is worth noting. By building the token infrastructure that connects payment providers to AI agents, Stripe is positioning itself as the essential middleware layer for agentic commerce. The same way Stripe became the default payment infrastructure for internet businesses in the 2010s, SPTs represent a bid to become the default payment infrastructure for AI-driven commerce in the 2030s. Every payment provider that wants access to agent-executed transactions will likely need to integrate through this or a similar token layer.

The merchant perspective

For merchants, the immediate benefit is conversion protection. BNPL options consistently lift checkout conversion rates — Klarna’s own data suggests double-digit improvements in many categories. If AI agents cannot offer BNPL at checkout, merchants lose that conversion lift on every agent-executed transaction. As the share of agent-driven purchases grows, that revenue impact compounds.

The integration is designed to be invisible from the merchant’s side. If a business already accepts Klarna through Stripe, agentic transactions flow through the same infrastructure with no additional development work. This frictionless approach is deliberate — Stripe and Klarna are betting that the payment providers who require zero merchant effort to support agentic commerce will capture the market before competitors who demand new integrations.

What this signals for community banking and financial services

For financial institutions watching the agentic commerce trend, the Klarna-Stripe partnership illustrates a pattern that will repeat across financial services. When AI agents begin managing transactions on behalf of consumers, every financial product that requires interactive human authentication faces a compatibility problem. Mortgage applications, insurance quotes, investment trades, loan originations — any process designed around a human clicking through screens and providing real-time consent will need to be re-architected for a world where software acts on the consumer’s behalf.

The institutions that build token-based, API-first interfaces for their financial products will remain accessible to AI agents. Those that rely on legacy web forms and manual authentication flows will find themselves increasingly invisible to the automated systems through which a growing share of financial decisions are made.

The checkout is just the beginning. The infrastructure being built today for agentic payments will define how AI systems interact with the entire financial services stack for the next decade.

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