INTERVIEW
After acquisition: How banks can use AI to keep the customers they win
Shan Lodh, Head of AI at Hyperlayer, on agents, identity and trust ahead of his fireside chat at MoneyLIVE Nordic Banking 2026
1. AI is changing how banks win customers. Once they're won, where should banks be focusing their attention?
On keeping them. A lot of the AI conversation is rightly about discovery and cheaper acquisition, but that is only half the task for banks. Customers rarely switch banks outright. They quietly quit instead, opening secondary accounts where they focus their spending, engagement and data.
Customers will use conversational AI to ask for sophisticated money management and personalisation tools that the bank will need to act on. Most banking systems weren’t built for that, so will need to focus on developing accounts that run on rules the customer sets, within the bank’s guardrails, because that’s how to keep those hard won relationships over the long-term.
2. Agentic search, commerce and payments often get used interchangeably. How do you separate them, and where does the bank fit?
They’re three steps in one journey. Agentic search is an agent finding and comparing options. Agentic commerce is that agent choosing and buying. Agentic payments is where money moves, and that’s where the bank comes in. In September PayPal announced that Meta’s agent will search and pay across its merchant network behind a confirm button. The one asking to pay is no longer the person who owns the account.
Networks, wallets, and agent platforms are building the rails and screening ahead of the payment. For account-based payments the bank still decides whether to approve it, and it is usually the one answering when an agent gets it wrong. Whatever happens upstream, the purchase ends with a request to the bank.
3. If agents start shopping and paying on customers' behalf, what has to change inside the bank?
Each agent needs its own identity on the system of record, so the bank knows which agent is acting, for which customer, and with what permissions. Know Your Agent asks of agents what Know Your Customer asks of people, though it is still emerging and has no common standard. Network programmes tell the bank who is asking. The bank still has to decide what the money may do.
We see an agent as a delegate with a mandate. Banks already handle delegation, such as a parent setting limits on a child’s card. The difference is that an agent has no legal standing of its own, so the mandate stays the customer’s and the bank needs a clear record of what was granted: who was permitted to do what, with which funds, under what conditions and until when. That mandate should be scoped, revocable, and enforced as money moves.
None of this needs a new core. It needs a layer above it where those permissions live, working with the identity infrastructure banks already rely on, such as MitID.
4. Once a customer has landed, how can banks use AI to keep them coming back?
By making money work the way that customer wants. That means accounts that act on rules the customer sets: a sweep into savings when salary lands, a shared pot with different rights for different people, a children’s card with limits a parent controls, and money that moves easily between wealth, family and personal current accounts.
AI makes those rules easier to set. A customer can say, “Put 10% of my salary towards a house deposit and tell me if I’m off track,” and that becomes a live rule running across the bank’s systems. Plain language becomes policy.
The rules also show intent. Customers who name their pots, create goals and set their own rules are telling the bank what they want in their own words, and declared intent is a cleaner signal than inferred behaviour. A rent pot that stops being funded is worth a nudge weeks before arrears appear. Insights should suggest, not advise, and should show customers how their behaviour and plans map to rules they choose. Parents in our Kids and Money whitepaper said they want AI as a transparent co-pilot rather than an opaque optimiser, which is a sound starting point for every bank and customer.
5. Where does the trust come from when an AI is acting on a customer's money, and what should banks be doing now?
Trust comes from deterministic enforcement. A model can propose, but the model shouldn’t decide what an agent may do. That decision belongs in a deterministic layer at the account, with limits the bank sets. Banks can keep using probabilistic models for risk scoring, but the mandate itself is a hard rule.
Anything an agent asserts is untrusted input, including instructions slipped in by a malicious web page or message. A firewall checks the merchant, the item, the mandate, the funds, and the customer’s history before money moves, and every decision is recorded so it can be replayed for a customer or a regulator. The model can change as often as the bank likes while the limits stay put.
As for what to do now, give each agent an identity on your system of record and put controls at the account, in the transaction path. An account that can say “this agent may spend up to DKK 1,500 a week on groceries, and nothing else” is one customers will trust with an agent.
Shan Lodh is Head of AI at Hyperlayer, the programmable layer that lets banks launch new products at speed without replacing the core they trust. He leads Hyperlayer’s AI strategy and technical delivery.
Shan has more than 20 years of experience building data and AI platforms across regulated financial services and technology. He was previously Head of AI Architecture at Allvue Systems, and before that held senior data and engineering roles at The Economist Intelligence Unit, Shawbrook Bank and Bottomline Technologies.
An economist by training, with degrees from Cambridge and Oxford, he brings that experience to helping banks turn customer and AI-agent instructions into controlled, auditable financial actions.
Hyperlayer is the programmable banking platform that enables financial institutions to innovate at pace without replacing the core systems that underpin stability. Its configurable design enables banks, credit unions, wealth managers, and merchants to launch innovative financial products – including agentic solutions – quickly and cost-effectively while maintaining compliance and control.
Hyperlayer works with Tier 1 through digital-native financial institutions to deliver highly personalized banking experiences across family banking, wealth management, RegTech, smart savings, and more, and is the technology behind the award-winning UK consumer money management and family banking app, HyperJar. A member of the prestigious and highly competitive global Mastercard Start Path program, the company closed one of the biggest early-stage funding rounds for a European fintech in Q3 2025, raising $40M to support global expansion as demand for its plug-and-play financial technology grows.
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