Artificial intelligence
In less than a decade, fintech has transformed financial services far beyond simple “smartphone banking”. Driven by the mass adoption of mobile apps, the standardization of APIs and a more open regulatory environment (open banking, DSP2, DSP3 on the horizon), players born of digital technology have redefined the banking experience: account opening in minutes, cards that can be configured in real time, instant payments, intuitive reporting, “no-ops” automations. Traditional banks still have decisive strengths – balance sheet strength, risk control, a comprehensive range of offerings – but they must now convert these strengths into seamless, impeccable customer experiences between physical and digital channels.
In this new landscape, two European champions illustrate the diversity of winning strategies. Revolut is on a financial “super-app” trajectory, targeting both the general public and professionals with a wide range of banking and para-banking services (savings, investment, foreign exchange, insurance, travel, merchant acceptance). Qonto, on the other hand, has opted for B2B specialization, with a “pro-first” proposition that aims to become the administrative and financial cockpit for the self-employed, VSEs and SMEs. Their common denominator is clear: Artificial Intelligence (AI) is no longer a technological add-on, it’s the driving force behind personalization, fraud prevention and operational excellence – at the heart of customer service, compliance and product journeys. (For a local supplier panorama, see our best AI agencies Paris and best consultancies by sector).
Revolut has passed a milestone of scale. In 2024, the company claims 52.5 million customers, +72% revenue growth to £3.1 billion, and a net profit of £790 million. Deposits topped the £30 billion mark, and annual volumes processed topped the £1,000 billion mark. Beyond these figures, it’s the structure of growth that catches the eye: the company doesn’t depend on a single product, but on a diversified mix combining accounts, cards, savings, investments (ETFs, equities, bonds), international transfer and foreign exchange, Revolut Business and a fast-growing merchant acceptance business.
This breadth is underpinned by an ambitious licensing and geographic expansion strategy (banking license in several EEA countries, progress towards “mobilization” in the UK), as well as uncommon product velocity: multiplication of micro-launches, rapid iteration, massive A/B testing. Revolut optimizes customer lifetime value through cross-selling: each relationship initiated on one usage (e.g. a card abroad) is patiently extended to other verticals (savings at promotional rates, micro-investment, travel insurance, targeted cashback, etc.). The broader the palette, the more the mobile application becomes the default destination for everyday financial needs – a virtuous circle in which usage feeds data, data feeds personalization, and personalization reinforces usage. (On the stack side, many companies structure these journeys with an AI agent platform and robust agent orchestration).
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Revolut’s value proposition is increasingly based on front-to-back AI, i.e. integrated at every stage of the chain:
Protection & anti-fraud.
Machine learning models analyze in real time the context of a transaction (amount, geolocation, device, usage pattern, network of beneficiaries) to preventively block operations deemed risky. By 2024, Revolut claims to have prevented over £600 million in fraud. The company is also tackling “authorized” fraud (APP), where the victim himself carries out the transfer under the influence: the aim is to detect the social engineering pattern (sequence of events, unusual interactions with customer service, sudden creation of new beneficiaries) and trigger an appropriate “hold & challenge”. (Effectiveness also depends on good agentic architecture and robust agent governance for quality control).
Experience & support.
Revolut has industrialized an “agent-grade” chatbot and deployed a co-pilot for its support teams, reducing resolution times by 80% on a large proportion of tickets. As a result, human customer service can focus on sensitive or complex cases, while recurring requests are handled in a frictionless self-service mode. The benefit is twofold: lower operating costs, higher customer satisfaction (See: AI agent vs chatbot and AI agents vs assistants to choose the right paradigm: agent/assistant comparison).
Personalized financial advice.
A consumer AI assistant, announced at the end of 2024, should gradually accompany financial decisions in the app: finer categorization, cash flow alerts, dynamic micro-budgets, savings recommendations, “nudges” to reduce invisible costs. The ambition is not just to inform, but to coach the customer at the right moment, with safeguards in line with local regulatory constraints. (In terms of design choices, clarifying agents vs. agentic AI avoids vocabulary confusion: agents vs. agentic AI).
Technological capabilities & partnerships.
Revolut has strengthened its cloud partnership (Google Cloud) to exploit latest-generation models (Gemini family) around fraud detection, personalization and automation. As the user base expands (target of 100 million customers evoked), the challenge becomes scalability: orchestrating massive data pipelines, monitoring model drift, ensuring explicability and operational-grade resilience (MLOps, monitoring, A/B on models, ethical safeguards). (To support execution: AI agent management, AI agent infrastructure/platform, and when relevant, an AI agent creator or agent marketplace to accelerate time-to-value).
In short, AI at Revolut is not a marketing veneer: it reduces risk, lowers the marginal cost of service and increases perceived value. It’s the backbone of the promise.
The opposite of a generalist “super-app”, Qonto has built a pro-first solution. With 600,000 customers in Europe and an application for a banking license in France to broaden the spectrum (credit, savings, investment), the scale-up has established itself as the daily companion for managers of small structures. The foundations: pro account, cards, transfers, multiple IBANs, multi-level rights and validations, fluid expense reports, integrated invoicing, semi-automatic reconciliation and own exports to accounting software. The goal: less admin, more business.
On the financial front, Qonto is claiming a second profitable year in 2024, with ~449 M€ in revenues and 144 M€ in profit. This performance is explained by a focus on usage: each product improvement – for example, automated receipt capture, intelligent expense categorization or fine-tuned delegation of payment powers – saves teams hours and makes the data intended for the accounting firm more reliable. This is a powerful differentiator in a world where time scarcity is the number one constraint for SME managers.
When it comes to AI, Qonto is moving forward with pragmatic bricks:
– an in-house chatbot already autonomous for over 50% of contacts, enabling faster responses without sacrificing quality,
– a “Qonto Intelligence” layer that activates recommendations and automations (expense anomaly detection, intelligent reminders, contextualized validations),
– opening up to specialized AI agents (e.g. automatic invoice retrieval), to eliminate re-keying and reduce the cost of monthly closing. (Here, no-code AI agent approaches via an AI agent creator and good multi-agent coordination streamline workflows.)
Here again, AI is not a gadget: it interferes in the day-to-day mechanics of the pro account, exactly where measurable productivity gains are created.
Amplitude vs. focus.
Revolut maximizes functional amplitude: B2C + B2B, payments, foreign exchange, savings, investment, insurance, travel, merchant acceptance. The assumption is that a universal platform captures more usage and arbitrage opportunities at the expense of competing offerings – including those from traditional banks. Conversely, Qonto focuses on depth of execution in one segment (professionals): less dispersion, more product precision and expense governance.
Monetization & LTV.
Revolut increases customer lifetime value via cross-sell and subscription tiers (premium features, cashbacks, travel benefits, trading). Qonto favors clear pricing, focused on high-frequency banking and administrative services, where each improvement reduces a real cost (time, errors, reconciliation, VAT, DSO).
Front-to-back AI.
Both players place AI at their core: Revolut on fraud, personalization and support co-pilots; Qonto on ops, support and accounting data reliability (where every minute saved counts). In both cases, AI lowers costs and speeds up resolution – two essential levers for a smoother customer experience on both mobile and web applications. (For a more in-depth look at the paradigm: agent vs. agentic AI.)
Licensing & compliance.
Revolut is advancing country by country, with multi-jurisdictional licenses and offers tailored to local constraints. Qonto, by applying for a banking license in France, is giving itself the opportunity to extend its spectrum while remaining pro-centric. Traditional banks, on the other hand, start from an advantage in compliance and risk management – but need to express this in legible, real-time digital pathways to remain competitive.
Relationship & service.
Revolut benefits from powerful self-service and co-pilots who absorb a large part of the flow; Qonto embodies resolution-oriented customer service in a B2B context (authorizations, mandates, validations). Traditional banks retain their human relationships for complex life-cycle issues (credit, assets), but need to gain in speed, clarity and digital proactivity.
Mobile experience.
At Revolut, the mobile app is the “gateway” to a multi-use super-app. At Qonto, the same app acts as a pro cockpit: cash, payments, receipts, invoicing, accounting – everything converges there. The promise is different, but the bar is the same: immediacy, control, transparency.
Revolut validates the super-app gamble with financial and usage fundamentals to match: 52.5M customers, +72% revenues to £3.1bn, £790m net profit, ~£30bn deposits and >£1,000bn volumes processed by 2024. AI is not cosmetic: ~£600m of fraud avoided and -80% on resolution times via chatbot/copilot, personalization engines and contextual anti-fraud. By extending this trajectory (multi-country licenses, product expansion), the implicit objective is scalability to 100 M customers, while lowering the marginal cost of service. The message for managers: the advantage comes from data-product-compliance orchestration, not from a single functional “hit”. (To capture demand, think also about GEO (Generative Engine Optimization) on the visibility side: GEO – AI Overviews/LLM SEO).
Are you wondering how to adapt Revolut’s “super-app” growth model and AI innovations to your organization? Contact our teams of experts today. (Or discover our “agents” pages: AI agent platform, AI agent management, agentic architecture).