How banks in France and Benelux can stay in the customer relationship as AI agents compete for the interface
Executive summary
Myth: to compete in the AI era, a bank must first modernise its core, then put AI on top.
Reality: AI moves value to whoever holds the customer's intent and can be trusted to act on it. A bank that builds down from the customer outcome can hold that position in two ways: through its own agent, and as the trust layer other agents rely on.
Situation. Banks in France and Benelux spend most of their technology budget standing still. Nearly 70% of bank IT spend runs existing systems, and France's cost-to-income ratio is the third-highest in the EU/EEA.
Complication. AI agents can now deliver the advice and sense of control once reserved for private banking. Open finance lets them reach a bank's customers without owning a core. On current planning assumptions, the rules that shape this market take effect in 15 to 21 months.
Resolution. Build one AI-native platform with two products: the bank's own agent (market A) and a trust layer beneath every agent (market B), built around verified financial claims. Market B pays off whichever agent the customer chooses.
The board's first 90 days:
- Approve an AI North Star within 30 to 45 days: a customer outcome, not a system.
- Launch a 90-day, read-only PFA pilot with a protected team and explicit kill criteria.
- Start the trust layer in parallel, as infrastructure: the permission dashboard and verified claims.
- Relabel the change portfolio and target at least 40% of change spend on the apex within 12 months. Keep funding the core where a named need requires it.
- Approve an autonomy and liability policy before any release can move money.
This is a strategic hypothesis, not a forecast. Section 3.5 names the signals that would disprove it.
1. Situation: banks spend to stand still
Banks in France and Benelux do not lack technology spend. They lack spend a customer can see.
Nearly 70% of bank IT spending goes to running existing systems and meeting regulation (evidence; a global figure from Accenture). Banking technology costs have grown about four times faster than revenue over 15 years. Rising rates hid this drift: EU bank income rose about 40% from 2021, costs about 22%. As rates fall, the drift is likely to show in the cost-to-income ratio (interpretation).
France already has the third-highest ratio of 30 EU/EEA countries, at about 65%. Belgium is at about 58% and the Netherlands at 53%, against 39% to 48% for Nordic banks. Staff, branches and AML all contribute to the ratio. The narrower point is that where it is high, there is less room to fund anything new.
Exhibit 1. Cost-to-income ratio by country, March 2026
[Image to come: EBA Risk Dashboard Q1 2026, p.13 · country weighted averages read from the chart, approximate]
Regulation adds to the run burden. DORA has applied since January 2025, PSD3 and the PSR were agreed in April 2026, and the new EU anti-money-laundering authority starts direct supervision in 2028. Most of it lands on the same legacy cores.
What the rest of the change budget buys is mostly functional value: faster payments, lower fees, fewer clicks. Instant payments, open APIs and cloud make these cheap for anyone to copy. On Bain's Elements of Value pyramid, the value that makes a relationship hard to replace sits higher: reducing anxiety, providing hope, a sense of control. Two of the five elements that most drive a bank's Net Promoter Score already sit above the functional tier (evidence; Bain, 2018).
This is not an argument against modernising the core. The trap is narrower: making customer value wait for the core programme to finish.
Detail: Appendix A1 (the modernisation trap) and A2 (Bain's Elements of Value).
2. Complication: AI moves value above the bank
2.1 AI makes the apex deliverable to everyone
Emotional and life-changing value used to need a human adviser who knew the customer's whole situation, so only wealthy clients got it. An AI agent with consented access to a customer's full financial picture could offer it to everyone, at close to zero marginal cost: "Yes, you can afford this, and here is why." Whether that lowers anxiety and changes behaviour is a hypothesis to measure, not a given.
The apex is the customer-facing point where intent, context and value are coordinated. The provider that reliably delivers upper-tier value there is best placed to hold the relationship (hypothesis).
Detail: Appendix A2.3.
2.2 The pattern has happened before
Each technology wave since 1980 turned a capability into a commodity, and value moved above it. The web, mobile and cloud waves each produced local challengers that rented the new commodity: Boursorama, bunq, Adyen. Incumbents kept investing in the layer that had just become a commodity. Wardley calls this inertia, and the 70% maintenance share is consistent with it (interpretation).
In this reading, the AI wave does something the earlier waves did not: it reopens most banking capabilities as uncharted ground, while infrastructure stays a commodity. Nobody owns that ground yet.
Exhibit 2. Map 1: nine banking capabilities after each technology wave
[Image to come: Wardley Map 1 · position of each capability after each wave · from the FOB-analysis workbook]
Detail: Appendix B1.
2.3 The bypass: agents reach customers without a core
Open finance lets a licensed third party read a customer's accounts and initiate payments without a ledger, a core or a branch. By September 2025 there were 537 authorised third-party providers across the EEA and UK. The PSR requires dedicated interfaces with performance parity, bans listed obstacles and introduces permission dashboards. FiDA would extend access to savings, pensions and insurance.
Read access is broad, but the right to act is still narrow: strong authentication needs a known amount and payee. So today the risk is mainly about who holds the customer's attention, intent and advice. The scenario to guard against is baggage disintermediation: an agent captures the relationship while the bank keeps the ledger, the capital, the AML duty and the DORA bill (hypothesis). No bank can see how far this has gone, because third-party API traffic is not reported.
Detail: Appendix B2.
2.4 The window: 15 to 21 months
On current planning assumptions, the AI Act's high-risk rules apply from December 2027 and the PSR's core obligations around H1 2028: 15 to 21 months from September 2026. A bank that ships purpose-bound consent, explained answers and graduated autonomy by then turns compliance into the product. A bank that arrives late complies anyway, and may pay for an interface its customers use through someone else. Both dates can move, so re-check them at each roadmap gate.
Detail: Appendix C5.
3. Resolution: one platform, two products
The bypass is real but bounded. The response is one AI-native banking platform with two products, built outcome-led rather than core-led.
3.1 Two markets
Market A is the customer's interface: the bank offers its own agent, starting with a Personal Finance Agent (PFA). Market B is the trust layer beneath every agent: identity, verified claims, consent, delegation and liability. A bank should play both. Market B pays off even if market A is lost, so the strategy does not depend on predicting which AI interface wins.
Exhibit 3. One platform, two products
[Image to come: One platform, two products · A: the bank's own agent · B: the trusted layer beneath every agent]
Banks start ahead on what market B sells: assurance, and a party that pays if something is wrong. They start behind on the trustworthiness of their AI, which the bank's own agent must prove release by release (interpretation).
Detail: Appendix C1.1 to C1.4.
3.2 Verified claims: from data holder to fact issuer
Instead of sharing raw statements, the bank issues signed, purpose-bound, revocable facts to any agent the customer authorises: income, affordability, account ownership, payment authority. When an agent applies for a mortgage, the lender does not need 12 months of transactions. It needs one fact, "net income above €3,000 a month, recurring for 12 months", signed by a party that pays if it is wrong. Whoever runs the journey, the bank stays in the path of every action. That this is the bank's lasting edge is the paper's central hypothesis. It holds only if claims become infrastructure that outside agents must call in high-value journeys, not a feature of the bank's own agent.
Detail: Appendix C1.5 and C1.6; claim 14 in Appendix D4.
3.3 Set a North Star and fund the apex
A North Star is a customer outcome that AI makes possible, stated plainly enough to veto a programme. For example: "Every customer has a trusted agent that runs their financial life, across every provider." Map the components it needs, and place each on the evolution axis: build, buy, rent or contain. Then give every running programme one label: apex, enabler, mandatory, economic or legacy drag. Stop or shrink legacy drag.
A working target is at least 40% of change spend on apex components. Track two numbers each quarter at board level: the run-versus-change split and the apex share. The drift bends only when the apex share rises.
Detail: Appendix C2; North Stars for every product line in C4.
3.4 Proof point: the Personal Finance Agent
The PFA answers one need in the customer's words: "Help me feel in control of my money, across every account I have, without having to manage it."
| Core-led | Outcome-led | |
|---|---|---|
| Starting point | The core system | The customer outcome |
| Data | Harmonise everything first | Read scoped data through a semantic control plane |
| Scope | The bank's own accounts | Every provider the customer consents to |
| First customer value | Years (working estimate) | Quarters (hypothesis) |
Outcome-led delivery builds three apex components first: a personal context graph, a purpose-bound consent router and a semantic control plane over legacy data. Autonomy climbs a seven-rung ladder gated by risk and reversibility, and the first release is read-only. The core keeps its investment where resilience, regulation, economics or a named outcome requires it, but it no longer gates customer value.
An illustrative model, not an estimate, nets €30 per active user per year. Retention and product take-up carry most of that value, so the pilot must measure them first. Its metrics are kill criteria: if by month 9 the PFA does not beat a free general assistant on control and trust, the bank shifts weight to market B.
Detail: Appendix C3.
3.5 Guardrails, and how we would know we are wrong
Outcome-led delivery moves risk rather than removing it: model errors, answers that cannot be explained, and liability for agent actions. The design answers each with maths verifiers, evidence behind every answer and graduated autonomy.
This paper is a strategic hypothesis, not a forecast. Two signals would weaken it most: customers do not adopt financial agents, or verified claims become a commodity anyone can issue. If the indicators turn, shift weight from market A to market B rather than abandon the outcome-led approach.
Detail: Appendix C5 and C6; every load-bearing claim is in D4. The control stack, liability positions and a stress test against legacy cores are in the Technical annex.
3.6 Roadmap and board decisions
The first 90 days set how the bank competes: with its own agent, as the trust layer beneath other agents, or both. The roadmap has two gates, "North Star signed off" and "usage and consent targets met", and lands the first PFA releases, piloted where digital channels are cleanest, before the regulatory window closes. The board approves eight resolutions, from the North Star and the PFA investment envelope to an autonomy policy and model exit plans. Progress is measured in customer outcomes, not foundations laid.
Core systems will always need investment, and regulation will make sure they get it. The question for every board in France and Benelux is what the rest of the change budget builds.
Detail: Appendix C7; where to pilot in A1.5.
The appendix
The evidence and working behind this paper are published as separate parts:
- Appendix A: Situation evidence: The modernisation trap and Bain's Elements of Value: the evidence behind the situation.
- Appendix B: Complication evidence: Wardley maps of five technology waves, and how AI-native challengers reach customers without a core.
- Appendix C1–C2: One platform, two products, and the method: The trust stack, verified financial claims, and how to set a North Star.
- Appendix C3: The Personal Finance Agent: Core-led versus outcome-led delivery of a Personal Finance Agent, with kill criteria and economics.
- Appendix C4–C7: Product lines, guardrails and roadmap: North Stars for every product line, feasibility in large groups, regulation, falsifiers and the roadmap.
- Appendix D: Glossary, sources and claims register: Glossary, sources, the author's related work and the register of every load-bearing claim.
- Technical annex: Agent control, liability and failure: Agent control architecture, autonomy ladder, liability positions, failure scenarios and a legacy-core stress test.
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