Banking.
Acquire, personalise, and retain, with each customer in control of the data behind it.
In retail and wealth banking, the contest is for the customer relationship: acquiring the right clients, personalising every interaction, and keeping them from switching. That runs on transaction and behavioural data, the most revealing signal a person emits, so we design to hold as little of it as possible: personalisation runs at the edge wherever it can, without that data reaching our servers, and each customer stays in control of the profile built from their own behaviour.
- R01Personalisation drifting into automated decisions under GDPR Art. 22 and consumer-duty rules
- R02First-party consent and lawful basis for cross-sell on transaction data
- R03Customer and behavioural data crossing extraterritorial jurisdictions (DORA third-party risk)
What's actually
reshaping the sector.
The shifts our research is tracking, named, current, cited.
DBS runs hyper-personalised nudges to customers and relationship managers as an always-on system, not a campaign, and the nudges to relationship managers alone drove a 16% lift in client engagement. That is the bar your customers are being taught to expect.
DBS Bank →McKinsey finds banks that centralise customer-value-based personalisation earn 5 to 15% higher revenue from campaigns, and shift the metric from products sold to retention and advocacy.
McKinsey →BBVA completed the global rollout of its ADA data-and-AI platform specifically to power generative-AI personalised customer services. The lesson: the personalisation is only as good, and as sovereign, as the data foundation beneath it.
BBVA →Databricks, our partner, frames banking AI as 'AI on your data', proprietary, not a third-party black box, with Unity Catalog keeping every model and agent cataloged, permissioned, and auditable for a regulator (HSBC runs personalisation at scale on it). That is our brief too: keep the model of your customer where a supervisor, and a DPO, can see it.
Databricks →DORA turns reliance on a few large AI and cloud providers into a resilience exposure: firms must manage concentration, hold an exit they can actually execute, and the ESAs now oversee the designated critical providers directly, most of them US clouds. Self-hosted, open-weight inference cuts that dependency at the source. The AI Act's high-risk tier and FiDA push the same way, which is why where your customer AI runs is now a board decision.
EUR-Lex (DORA) →In banking, the customer relationship, not the core ledger, is where AI and sovereignty compound: personalisation runs on data too revealing to hand to a third-country model.
Win the customer on data a competitor can never see.
Retail and wealth banking growth is a personalisation problem now: acquiring the right customers, guiding the next best action, and keeping them from switching. Every one of those runs on transaction and behavioural data, the most revealing signal a person emits. A bank that lets that signal leave its perimeter has given away the one edge a rival cannot buy.
Personalise onboarding, cross-sell, and relationship management on sovereign, first-party data and you lift revenue and retention on the campaigns where a few points compound, without a transfer or consumer-duty problem. Run them on an opaque US API and you have handed a competitor the one signal that was yours alone.
We build the sovereign customer-data and personalisation stack: first-party acquisition, next-best-action, and RM copilots decisioned on EU compute, with frontier speed reserved for non-personal creative. In banking, the relationship is the franchise; sovereignty is how you keep it growing.
Where each AI workload
should live.
| Workload | Placement | Rationale |
|---|---|---|
| Acquisition & onboarding personalisation | Sovereign | Built on first-party customer data; belongs on EU compute. |
| Next-best-action & cross-sell | Sovereign | Decisioned on transaction history that must not leave the perimeter. |
| Relationship-manager copilots | Sovereign | Client PII and portfolio context; long retention. |
| Marketing creative & campaign copy | Frontier | Non-personal, fast iteration. |
What is yours to encode,
and what you are renting by accident.
Before any platform decision, one question settles the rest: how much of what your organisation knows can be written down precisely enough for a machine to act on it, and who ends up owning that writing. We run a short engagement to answer it, and it produces working artefacts rather than a report.
Two artefacts come out of this. An operating ontology, the nouns: the entities, relationships, and states your business actually runs on, described once and precisely. And agentic skills, the verbs: the procedures, thresholds, and judgments your best people apply, written down, versioned, and testable instead of retold.
A placement map addressed to the CTO or CIO office: what runs on your compute, what is bought, what is rented, who holds the keys, and what each costs. One test cuts through most of the debate. If a supplier vanished on a Friday, what stops working on Monday, and how long would it take you to replace it?
Where an open-weight model fine-tuned or adapted on your own corpus beats a frontier call, where a small purpose-built model for one narrow task beats both, and where the frontier still earns its fee. Geographic sovereignty falls out of that answer rather than having to be argued for on its own.
- →Credit and eligibility policy: the criteria, the exceptions, and who may grant them
- →Suitability and consumer-duty language, with the reasoning a regulator would ask for
- →Complaint handling: what counts as detriment, and the remedy ladder that follows
- →The relationship manager's playbook: which signal triggers which conversation
- →Fraud and financial-crime triage rules that today live in analysts' instincts
Transaction narratives are a corpus nobody else holds. A small open-weight model fine-tuned on your own decisioned cases beats a frontier call on classification, at a fraction of the cost, and never leaves the perimeter.
- +A register of encodable know-how, ranked by value and by how fast it decays
- +A draft operating ontology in an open format, not inside a vendor's platform
- +Two or three working skills, built and evaluated on your real work, not demonstrated on a slide
- +A bring-it-in-house plan the CTO or CIO office can actually staff
- +A model strategy: open weights, fine-tuning, purpose-built small models, and where the frontier stays
- +An evaluation harness that shows it works, and keeps showing it after we leave
This is a CTO and CIO office engagement, not a procurement exercise. Your own people have to end up running it, because the moment the encoding is delegated, the thing being encoded quietly stops being yours.
The enterprise-AI platforms call this layer an ontology, and the idea is right: a governed model of your objects, links, and actions that agents can act on. It lives inside their product. A forward-deployed engineering shop will design, build, host, and maintain the software around it for you. Both are serious, and both leave the same question open. At the end of it, who owns the layer that holds your judgment? We build the same artefacts in open formats, on compute you control, and hand your CTO the keys.
The customer stack,
inside the bank.
The same four layers we ship in legal, instantiated for a bank. Here the application layer is built with you rather than bought off a shelf, and it belongs to you when the engagement ends.
A first-party identity and consent spine, a next-best-action console the marketing team can operate without a data scientist in the room, and a relationship-manager workspace grounded in the client's own portfolio and correspondence.
Eligibility criteria, suitability and consumer-duty language, complaint handling, and tone of voice, written once as versioned skills the agents load, reviewable by compliance, rather than buried in prompts nobody can audit.
Routing by sensitivity: anything decisioned on transaction or behavioural data stays on sovereign compute by architecture, non-personal creative work goes to the frontier under zero-retention terms, and every call is logged against the customer record.
In your own data centre, on an isolated instance we operate on sovereign infrastructure, or on dedicated AI hardware inside the bank's perimeter for the models that score transaction data.
The application layer here is built for you, not bought. The pattern is the one we already ship, as a product, in legal.
Four postures.
One codebase.
The same software in every posture, so a team can start hosted and end up air-gapped without a migration project. You move along the ladder when you decide to, not when a contract renews.
Your stack stood up on our infrastructure in days and pointed at real work, so it is judged on your matters rather than on a demo. The pilot fee credits against the hardware when you migrate.
Teams that want proof before capital expenditure.
The open-source cores installed on a machine you own, by a script anyone can read first. No account with us, no telemetry, no phone-home. Code flows one way, from us to you, and only when you ask for it.
Small teams, and anyone who wants to inspect the source before trusting it.
An isolated instance per client on sovereign infrastructure, European by default, run, patched, and monitored by us, with the security and backup credentials in your hands rather than ours.
Organisations without an infrastructure team to spare.
Dedicated AI hardware inside your building, with the applications and the open-weight models you choose installed and tested before it ships. It runs with the network unplugged, and it keeps working whatever happens to us.
In banking the appliance usually sits beside the analytics estate, scoring the data that is too revealing to send anywhere.
Work that cannot leave the building, ever.
- +Deployment and hardening, on your machines or ours
- +Knowledge encoding: your playbooks and positions compiled into installable skills
- +Model selection, tuning, and an evaluation harness built on your own work
- +Remote maintenance and monitoring at a published monthly rate
- +Updates as ordered, readable migrations, always after an encrypted backup
- +Backup, restore, and continuity drills you can actually rehearse
- +Enablement for the people who use it daily, not only for IT
- +Governance evidence: routing logs, audit trail, and an attestation pack
- Reach into your instance or see your work
- Revoke your software or disable a licence remotely
- Force an update on you
Not promises. Consequences of the architecture: your keys and your data are generated on your machine and stay there, and nothing calls home.
The trusted bank
starts by helping
your customers spend less.
A bank's moat is the relationship, and the relationship runs on trust the old cross-sell never earned. The way in is to help before you sell, and to help with the thing customers find hardest to see: what they are actually paying, and what they are committed to.
firmas.io scans a customer's own bills, subscriptions and contracts and explains, in plain language, what they pay and what they are tied to, with renewal dates and a plain read on the risky clauses. It is local-first and zero-knowledge: the documents never leave the customer's device, so the advice is unarguably on their side.
A bank that puts this in front of its customers stops being the party that profits from confusion and becomes the one that clears it up. That is how a new kind of bank earns a relationship the old product push could not.
The same visibility surfaces the moments where a bank product is genuinely the better deal: refinance an expensive loan, consolidate, replace an overpriced insurance or energy contract, protect a life event, or put freed-up cash to work. Offered as next best actions through the sovereign agent loop, consent-based, these are the products that carry healthy margins, and they are welcome because the tool has just shown whose side the bank is on.
The app is the customer's, the data stays with them, and the decisioning runs on sovereign inference inside the bank's perimeter. Consumer empowerment and the bank's growth engine sit on the same privacy-first foundation, not in tension with each other.
Your customers get their contracts understood, their commitments tracked, and their documents kept on their own device.
The Agentic
Bank.
A marketing organisation rebuilt as one continuous loop, run by agents on data the bank never lets leave its control. How the sovereign stack redefines the practice, what the rebuild looks like, and a modelled result.
Four partners in perfect coordination: Databricks, Hightouch, Seal Metrics, Empathy.AI.