1) The evolution of AI agents 2) Digital Banking Landscape 3) Visa's agentic AI play

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

1) The evolution of AI agents

AI agents are the next big thing. Not in the future but now. They are fundamentally changing how we do things with developments moving at break-neck speed. Let’s take a look.

Agentic AI is the evolution of GenAI. Where ordinary GenAI merely suggests what to do, an agentic setup both suggests and executes independently.

The latest BCG report provides a fascinating glimpse of the latest trends – here is my summary:

1. It is getting faster and easier to build, deploy, and monitor agents

2. The first commercial agents are here, and generating meaningful revenue. Coding agents are among the first to reach product-market-fit.

3. Voice goes mainstream, native image gen and edit has gone viral, and video continues to evolve.

4. AI maturity is driving a shift from predefined workflows to self-directed agents.

5. We are headed for a multi-agent future where agents can work together in networks and with humans to accomplish complex tasks or automate multistep processes.

6. Organizations are already gaining significant value from agentic workflows (e.g. Bloomberg’s compliance agents reduce time-to-decision by 30-50%).

7. Increasing focus on multi-turn tasks requires agents to manage context, sequence actions, and adapt to evolving goals.

8. Today, AI Agents can reach ‘1h’ of automation - doubling every 7 months. If the trend continues to the end of this decade, AI systems will be capable of autonomously carrying out month-long projects.

9. The open-source Model Context Protocol (MCP) launched by Anthropic can be a huge game changer: it lets AI agents access the same data and tools wherever they run - across any cloud or application - eliminating platform lock-in. Already adopted by OpenAI, Microsoft, Google, Amazon.

10. MCP standardizes how an agent talks to systems and tools; Google’s A2A (and similar protocols) standardize how agents talk to each other - so together they cover both sides of the conversation: “agent ↔ system” and “agent ↔ agent.

Summary: Panagiotis Kriaris, Source: BCG, AI Agents, and the Model Context Protocol

2) Digital Banking Landscape

Digital banks are no longer niche-only start-ups. They have come of age, competing head-to-head with traditional banks. Here are the models and their playbook.

The news from Revolut’s 2024 financial results is still fresh: net profit of $1.4 bn (149%+ yoy) with a margin of 35.5%!

Nubank has 109 mn customers, more than double vs Barclays and 3+ as much as Societe Generale.

The success and proliferation of digital banking models around the globe is a clear sign of agile, digital-first, product-niche strategies prevailing over traditional, monolithic, vertical banking business models.

But they are no longer niche. They have massively evolved, both in terms of scope and scale.

Whereas different patterns can be identified in their evolutionary path, the successful models can be aggregated into two broad categories:

— 𝗚𝗿𝗲𝗲𝗻𝗳𝗶𝗲𝗹𝗱 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 starting completely from scratch by means of identifying a niche market or segment, often neglected by incumbents, and focusing on seamless customer experience, attractive design, competitive pricing and a digital or mobile only set-up. In terms of strategy two elements clearly stand-out: 1) hyper-growth and scale as the core - sometimes only - metrics (which explains why so many have been unprofitable) 2) an ecosystem play, driven by horizontal partnerships (vs the vertical traditional model). N26, Revolut and Nubank are typical examples of this model.

— 𝗟𝗮𝗿𝗴𝗲, 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 with a non-finance business geared on #technology and an anchor in e-commerce launching (digital) banking spin-offs as a means of converting (and monetizing) their existing client-base. Most (or almost all) of the examples here come from Asia (i.e. Webank, Kakaobank), mainly due to the set-up of the economy (lacking a traditional finance architecture and, in effect, benefiting private, BigTech players covering the gap). Webank, for example, is owned by Tencent, China’s largest social-media BigTech company (owner of WeChat, China’s equivalent of Facebook). It has managed to reach a $33 billion valuation and a base of more than 400 million active users by focusing on building a modern IT stack (as a competitive edge to traditional banks) and leveraging on the data generated by the Tencent ecosystem (i.e. retail lending credit scoring built on Tencent data, resulted in a non-performing loan ratio of just 1.2%, about half (or less) of the industry average for such non-secured loans).

Irrespective of their origins, both models have been (fast) converging to what has become the new holy grail of modern finance: platform economics and ecosystem plays.

Opinions: my own, Graphic source: C-Innovation

3) Visa's agentic AI play

The news just dropped - and it was only a matter of time. After PayPal and Mastercard, Visa is also going big on agentic AI. It’s called Visa Intelligent Commerce (VIC). Here is my take.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁?

VIC is a trust layer that lets autonomous AI agents - travel bots, voice assistants, smart fridges - find, decide and pay for consumers. Visa converts an ordinary card into an AI-ready token that:
• verifies the agent is authorised by the cardholder
• enforces spend limits and rules
• uses Visa’s real-time risk models to approve or block each transaction.

Visa will extend the infrastructure, standards and capabilities present in physical and digital commerce today to AI commerce. Consumers will enable AI agents via AI platforms to use a Visa credential (4.8 billion today) at any accepting merchant location (150 million) for any payment use case.

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗮𝗸𝗲 𝘀𝗲𝗻𝘀𝗲?

• AI is moving from chat to action. Autonomous agents are forecast to drive $1 trn in spend by 2030; the missing piece is a trusted “buy” button.

• Friction kills sales. Up to 70 % of mobile carts are abandoned; an agent that checks out in milliseconds fixes that.

• Visa leverages existing infrastructure built over decades (to combat fraud) and redeploys it for agent-driven commerce.

𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝘄𝗮𝘁𝗰𝗵

• Consumers: AI agents embedded in devices - from smartwatches to digital assistants - to shop on a consumer's behalf via programmable spending limits, merchant rules, and tokenised payments.

• Merchants & platforms: higher conversion and truly personalised storefronts built for “segments of one” (treating each individual customer as a unique segment).

• Banks & fintechs: new AI-ready cards with consent tools and dashboards, monetising agent insights.

• Developers: rails-as-a-service; expect an explosion of agent-first apps across travel, retail and SMB back-office - no deep compliance or full-stack checkout flows needed.

• Policy & privacy: tokenisation, spend limits, and audit trails offer a template regulators may adopt as autonomous commerce scales.

Visa isn’t trying to build the best AI - it’s ensuring any AI can pay safely. By opening its network as the last mile for autonomous agents, Visa positions itself as the invisible switchboard of the next commerce era. If AI becomes the new browser, Visa wants VIC to become its checkout button.

Opinions: my own, Video source: Visa