1) Agentic Commerce Plays 2) 2025 FinTech Overview 3) Is Stripe worth $140 bn? 4) BNPL Competition Map 5) From AI to MAS

Welcome to my newsletter! Each week a few hand-picked topics from the world of fintech, payments and banking with behind-the-scenes analysis!

1) Agentic Commerce Plays

Three very different strategies are emerging in agentic commerce. Each reflects a different bet on where power might sit when agents buy on our behalf.

𝟭. 𝗚𝗼𝗼𝗴𝗹𝗲 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗮𝗴𝗲𝗻𝘁 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗷𝗼𝘂𝗿𝗻𝗲𝘆:

• Google wants to define how AI agents discover products, compare options, access merchant information, and move into checkout.

• The ambition is to become the orchestration layer between AI assistants and merchants effectively shaping how buying decisions are structured before any money flow is triggered.

• If this works, Google will not only influence demand through search but also how transactions are initiated and routed.

• Tools: The Universal Commerce Protocol (a framework for how agents and merchants exchange structured commerce data), Gemini (Google’s AI assistant), and AI-enhanced Search.

𝟮. 𝗩𝗶𝘀𝗮 & 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗮𝗿𝗱 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗼𝗳 𝗽𝗮𝘆𝗺𝗲𝗻𝘁:

• The card networks are taking a more focused approach: they are concentrating on what happens when an AI agent is authorised to spend money.

• Their strategy is to ensure that, even if AI changes how shopping happens, the execution of the transaction still runs on their rails.

• They are extending existing models of identity, authentication, and fraud protection to software agents but they are not trying to control discovery or product comparison.

• Tools: Visa Intelligent Commerce and Mastercard Agent Pay. These are new frameworks that allow AI agents to be securely authorised to make payments using existing card network infrastructure.

𝟯. 𝗦𝘁𝗿𝗶𝗽𝗲 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲 𝗵𝗼𝘄 𝗮𝗴𝗲𝗻𝘁𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁:

• Stripe sees agentic commerce from an infrastructure perspective: when an agent decides to buy something, it needs a reliable, programmable way to complete the transaction.

• Rather than redefining the shopping journey or owning the payment network, Stripe focuses on making checkout easy for software to initiate.

• This positions Stripe as the execution engine regardless of whether discovery happens through Google, an assistant, or another platform.

• Tools: Stripe’s Checkout and payment APIs. These are widely used online payment tools that allow businesses (and now potentially AI agents) to trigger and manage payments through software.

Many see this as a race to build the best shopping assistant.

In reality, it’s a race to control the rails.

And we’ve seen this play before.

𝗦𝗼 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀:

Who is best positioned to own the rails this time?

Opinions and graphics: Panagiotis Kriaris

2) 2025 FinTech Overview

What happened in Fintech in 2025 and what’s behind it?

Here is my behind-the-scenes summary based on the FT Partners 2025 Annual FinTech Almanac numbers.

𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗻𝗴 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆:

• Capital is concentrating into fewer, larger rounds as investors back proven platforms over early-stage fintechs.

• Profitability and predictable revenue now matter more than growth alone, as higher cost of capital has reset how risk is priced.

• Financial Management and WealthTech attract capital because banks and asset managers are still modernising core workflows around data, reporting, risk, and operations.

• Crypto funding has shifted from speculation toward infrastructure.

• Payments’ reduced share of financing reflects maturity of the core rails, with innovation moving to embedded and vertical-specific use cases.

• Banking and lending funding is spread across specialised tools (onboarding, underwriting, compliance, servicing, etc) as most banks choose to modernise in layers and not by replacing their core in one go.

• InsurTech investment is rising as insurers face worsening loss ratios (driven by climate volatility, inflation, and fraud) and use software to regain control over pricing, underwriting, and claims.

• Capital is increasingly flowing to markets that combine fast-moving regulation, public-sector capital, and national digital rails (real-time payments, digital ID, open finance).

• Mega-rounds are returning but mainly for scaled leaders, meaning this is not a generic market trend but focused on a small group of companies that already behave like infrastructure.

𝗠&𝗔 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆:

• M&A activity is accelerating because many fintech categories are now mature, making consolidation the fastest way to expand.

• Scaled fintechs are increasingly the buyers, using acquisitions to add capabilities faster than they could build internally.

• Acquisitions are focused on filling product gaps (risk, data, compliance, embedded payments, fraud) rather than buying growth.

• Payments M&A is driven by margin pressure and intense competition, with players buying scale and efficiency rather than chasing new geographies.

• Financial Management and WealthTech M&A is driven by demand for platforms that already sit at the centre of financial operations.

• Crypto M&A is selective, targeting regulated, compliant infrastructure rather than consumer-facing speculation.

• Cross-border M&A is rising as fintechs use acquisitions to enter regulated markets faster than licensing alone would allow.

• Private equity is accelerating as many strong fintechs generate cash but lack public-market scale, making them attractive candidates.

What are the trends that you see continuing in 2026? What did I miss?

Opinions: Panagiotis Kriaris, Graphic source: FT Partners

3) Is Stripe worth $140 bn?

Is Stripe really worth $140 bn? Latest reports suggest Stripe is exploring a tender offer at a $140bn valuation.

𝗪𝗵𝗼 𝗶𝘀 𝗦𝘁𝗿𝗶𝗽𝗲:

• Stripe provides software and infrastructure for businesses to accept and process payments.

• It is one of the largest online payment processors globally.

𝗧𝗵𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗿𝗲𝗰𝗶𝗽𝗲:

• Stripe started with a clear, underserved problem: accepting online payments was slow, clunky, and overly technical, so Stripe built a simple, developer‑friendly API anyone could integrate in minutes.

• Chose developers as the primary audience, with great documentation, self-serve onboarding, and minimal code required to go live.

• Removed traditional pricing and contracting friction with transparent fees and easy setup, pulling in startups first and later enterprises.

• Expanded horizontally from core payments into subscriptions, marketplaces, billing, fraud‑prevention, and other financial building blocks, turning itself into economic infrastructure for the internet.

• Grew through strategic partnerships (Shopify, Amazon, big SaaS platforms, etc.) and fast global roll‑out, embedding itself into the backbone of modern digital commerce networks.

𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗵𝗶𝘀𝘁𝗼𝗿𝘆:

• Mar 2021: $95 bn

Fuelled by COVID-driven online shopping surge and investor excitement for Stripe powering high-growth internet firms.

• Mar 2023: $50 bn

Rising interest rates deflated tech hype, forcing a valuation reset toward profitability and more realistic growth expectations.

• Feb 2024: $65 bn

Driven by improving margins, tighter cost discipline, and growing confidence in Stripe as durable financial infrastructure with a clear path to sustainable profitability.

• Feb 2025: $91.5 bn

Reflected continued margin expansion and investor confidence that Stripe’s growth is becoming more predictable, profitable, and resilient.

• Sep 2025: $107 bn

Driven by strong customer retention, expanding monetisation per customer, and confidence that new products (treasury, capital) deepen Stripe’s long-term moat.

𝗧𝗵𝗲 𝘁𝗲𝗻𝗱𝗲𝗿 𝗼𝗳𝗳𝗲𝗿:

A tender offer is a private transaction where existing shareholders (often employees and early investors) can sell some of their shares to new or existing investors.

𝗧𝗲𝗻𝗱𝗲𝗿 𝘃𝘀. 𝗜𝗣𝗢:

• An IPO would put Stripe into constant public pricing, heavy reporting requirements, and quarterly performance expectations.

• A tender lets Stripe give employees liquidity, avoid public-market volatility and lock-ups, and keep its focus on building product and platform.

• Stripe can control share price, buyer selection, and dilution, without raising capital given it is already profitable and cash-generative.

• Stripe gets liquidity and flexibility while keeping an IPO as a future option on its own terms.

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻:

Is an IPO off the table for 2026?

Opinions and graphics: Panagiotis Kriaris

4) BNPL Competition Map

BNPL is not a new offering. It is a new distribution model.

𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:

• BNPL is unsecured consumer lending embedded into checkout.

• In most markets, BNPL is synonymous with instalments; in some markets, invoice-based BNPL remains dominant (e.g. Germany)

• The BNPL provider underwrites the transaction in real time

• The provider, not the merchant, takes the credit risk

• Revenue comes from merchant fees, interest on longer-term plans, and late fees

𝗧𝗵𝗲 𝗸𝗲𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 vs traditional consumer credit is distribution:

• Traditional consumer credit is product-first: a credit card or a personal loan exists independently of the purchase and can be used across contexts.

• BNPL is context-first: credit is offered only at the moment of purchase, embedded directly into the checkout flow.

BNPL did not start as a platform strategy.

It started as a checkout optimisation and credit accessibility play.

𝗘𝗮𝗿𝗹𝘆 𝗽𝗶𝗼𝗻𝗲𝗲𝗿𝘀 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗶𝗱𝗲𝗮:

𝟭. Offer short-term, interest-free instalments

𝟮. Remove friction from ecommerce checkout

𝟯. Serve consumers who either lacked access to traditional credit, or preferred alternatives

The first modern wave was led by branded, consumer-facing specialists such as Klarna, Afterpay, and Affirm.

These companies built recognisable consumer brands and positioned themselves as alternative ways to pay.

𝗧𝗵𝗲𝗶𝗿 𝗲𝗮𝗿𝗹𝘆 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀:

1. Tight checkout integrations

2. Fast, lightweight underwriting

3. Clear consumer value proposition (simple, transparent, often 0%)

As adoption grew BNPL moved from a niche ecommerce option to a mainstream checkout method.

What began as a narrow payment-and-credit feature became a strategic entry point into commerce and consumer finance.

𝗧𝗼𝗱𝗮𝘆 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗵𝗮𝘀 𝘀𝗵𝗶𝗳𝘁𝗲𝗱:

• Pure BNPL carries high merchant pricing, but economics are sensitive to credit losses, funding costs, and scale effects

• Rising interest rates exposed the capital-intensive nature of BNPL models and pressured valuations

• Regulatory scrutiny has increased as BNPL is increasingly treated as consumer credit, raising compliance costs

• BNPL is now widely available as a feature (Bigtech, PSPs, banks), reducing product-level differentiation

As a result, branded BNPL providers are diverging into distinct strategic paths:

1. Consumer commerce and shopping platforms

2. Credit-first consumer finance companies

3. Checkout distribution and merchant-scale plays

4. Regional consumer finance ecosystems

𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗽𝗮𝗿𝘁?

• Global BNPL e-commerce value grew ~252× in a decade: from $2.3bn in 2014 to $580bn in 2024!

• Despite this growth, BNPL still accounts for only ~5% of global e-commerce.

Opinions and graphics: Panagiotis Kriaris

5) From AI to MAS

Everyone is talking about agentic AI and yet the next frontier is already in the making: Multi-Agent Systems (MAS).

AI didn’t arrive all at once – although in many cases it might seem it did. It evolved in distinct phases, each unlocking new capabilities and changing how work gets done:

𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜):

• Systems powering rule-based models and statistical inference to detect fraud, recommend investments, and process documents - all in response to human prompts.

• Financial Services (FS) example: Credit scoring models and fraud detection engines improved efficiency, but remained passive tools waiting on human input.

𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜):

• LLMs and foundation models that brought language fluency and contextual understanding. These systems can create, explain, and summarize - moving from data crunching to content generation.

• FS example: Chatbots that summarize regulatory filings, generate client reports, or support advisors with contextual investment narratives.

𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜:

• Systems that can interpret goals, plan actions, and operate independently within constraints. These agents shift the human role from executing tasks to defining intent.

• FS example: AI agents that autonomously rebalance portfolios based on client preferences and market movements - no human intervention required.

𝟰. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗠𝗔𝗦):

• MAS represent the next leap. Multiple agents - each specialized - work together, negotiate, and adapt in real time to achieve shared outcomes across environments.

• FS: Agents handling client onboarding, AML checks, credit assessment, and regulatory filings collaborate seamlessly to approve new clients in minutes.

𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:

MAS enable distributed, intelligent systems that can self-organize, learn continuously, and respond dynamically to change. They reduce operational bottlenecks and shift digital architectures from static pipelines to adaptive, event-driven systems.

𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀:

• Efficiency: MAS collapse multi-day processes into seconds - from KYC to loan origination.

• Mass hyper-personalization: Real-time tailoring of product decisions across customer journeys and risk contexts.

• Resilience: Distributed agents can recover from local failures, reroute tasks, and maintain service continuity without manual intervention.

• Compliance: Agents track regulatory changes and trigger operational updates autonomously.

MAS aren’t just the next step in AI - they’re how AI starts to really work like a system. Value will increasingly come from turning multiple models into coordinated workflows that handle entire process and journeys end-to-end.

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻:

What do you think are the biggest challenges for MAS adoption?

Opinions: Panagiotis Kriaris, Graphic source: Capgemini