Will AI agents cause the next corporate bank run?

Torsten Slok, Apollo’s Chief Economist, put out a short note recently asking whether an agentic bank run is coming. He argues that AI assistants could soon start moving household cash automatically into accounts paying over 3%, rather than leaving it in US checking accounts paying 0.1% on average. If enough people did that, banks would lose a lot of the cheap funding they rely on to lend.

Although he’s talking about households, I think something similar could eventually happen in corporate treasury, although it might take longer. The balances are much bigger, most sit well above any deposit insurance limit, and moving them means treasury policies, approvals, and an audit trail. Nobody wants to explain to their board that an agent moved £20m somewhere it shouldn’t have.

When it does happen, though, I think the impact on banks could be much bigger too. I don’t necessarily mean a bank run in the traditional sense. More likely, it means gradually removing some of the inertia that keeps corporate cash sitting in low-yielding bank accounts today.

How big is the gap?

In July, the average rate on UK companies’ interest-bearing instant-access deposits was 1.97%. In the euro area it was 0.60%.

For context, central bank rates today are 3.75% in the UK and 2.50% in the euro area. They aren’t directly comparable with the rates available on corporate deposits, but they give a sense of the gap. A 178 basis point difference on £10m, for example, is equivalent to £178,000 a year.

For banks, this spread matters a lot. US banks’ net interest margin was 3.32% in the second quarter, according to the FDIC. In the UK, the Bank of England says aggregate net interest margins across the major banks increased year-on-year in the first quarter, with UK-focused banks benefiting from higher structural hedge income. Across European banks, net interest margins are around 1.6%. Cheap deposits aren’t the only thing driving those margins, but they’re clearly valuable to banks. 

None of this is because treasurers aren’t paying attention. Nearly half of the organisations in the AFP’s 2026 benchmarking survey have fewer than five people in treasury. Moving cash somewhere new usually means another onboarding and KYC process, plus internal approvals, and there’s nearly always something more pressing that week.

It’s also worth saying that rate isn’t the only reason companies keep money with their relationship banks. Balances often sit alongside an RCF, better FX pricing, or access to capital markets business, and those relationships have real value. My view is that most teams just don’t have the time to work out how much of their balance the relationship genuinely needs, what could sit somewhere else, and what leaving it there is costing them. That’s the kind of analysis an agent could eventually run every day. In combination with access to secured institutional-grade products, treasurers could have an opportunity to generate enhanced risk-adjusted returns on their cash. 

Why it will take time

AI adoption will take time. Andrej Karpathy describes current models as having jagged intelligence, and anyone who uses them regularly will recognise that. They can do something genuinely impressive and then get something simple wrong.

Our own AI in Treasury Report, a survey of 55 treasury professionals, found only about a quarter use AI regularly. Cash flow forecasting is where they most want help, chosen by 73%, yet more than three quarters say accuracy is their biggest concern. Only 4% want AI involved in executing transactions. For now, treasurers want AI that supports decisions rather than one that carries them out, which I think is a reasonable position to hold today.

It is changing quickly though, with many companies looking to address issues with existing AI models. One interesting example is TypeSafe AI, which released Jev in early access this month. It isn’t a chatbot. It’s designed to take in messy information and return structured decisions. TypeSafe says it gives more consistent answers than a large language model and tells you how confident it is in each one.

It’s early, and those claims will need to be proven in practice, but the direction is interesting for treasury. Deciding where surplus cash should sit this week given your policy, existing exposures, and today’s rates is exactly the sort of structured problem that these systems should eventually be able to help with.

The other thing that will set the pace is infrastructure. An agent can only act on cash if the places holding it can be reached programmatically, with the right permissions and controls. Most corporate banking infrastructure wasn’t designed with autonomous agents in mind. My feeling is that platforms built API-first will enable these workflows much sooner, and that’s where treasurers will see the benefit first.

So I don’t expect anything like Silicon Valley Bank, where more than $40bn left in a single day. What seems more likely is a gradual shift. As more of the analysis gets automated, some of the inertia disappears. Banks then come under steady pressure on deposit margins and either pay more for corporate balances or find other ways to hold on to them.

Chasing yield isn’t enough

If you ask an agent to find the highest rate, it will do exactly that, and that’s not a good enough objective for corporate cash.

An unsecured deposit is effectively a loan to the bank. At the end of 2022, around 94% of SVB’s deposits were uninsured. A well-designed agent should be weighing up the same things a good treasurer does. How likely is this counterparty to default? What would we recover if it did? Are we being paid enough for that risk? And does putting more money there leave us too concentrated?

At TreasurySpring, we look at every product on a risk-adjusted basis for this reason, rather than treating the headline yield as the only thing that matters.

This is where I think we are well placed. A single onboarding gives you access to more than 120 counterparties and 1,100+ Fixed-Term Funds (FTFs), so you aren’t limited to the banks you already have accounts with. Many of those FTFs are secured by collateral through repo.

More importantly in the context of agents, that information is increasingly machine-readable. Our MCP server lets AI assistants see available FTFs, current yields and your portfolio, while our API allows other systems to connect directly to the platform. This lets clients either plug that data into their own agent or, over time, use an intelligence layer built natively into the TreasurySpring Portal, with the products, the risk analysis, and their treasury policy all in one place.

For the time being, I’d still want a person signing off on anything material, with the agent doing the analysis and the treasurer making the decision.

But that alone could change quite a lot. If an agent can continuously look at a company’s cash, its treasury policy, its existing bank relationships, counterparty risk, and the alternatives available, it removes a lot of the work that means this analysis doesn’t happen as often as it could today.

I don’t think the corporate version of an agentic bank run will look much like a bank run. It’ll probably be much slower. But if AI gradually makes it easier for treasury teams to work out how much cash genuinely needs to stay with their relationship banks, where the rest could go, and what they’re giving up by leaving it there, the end result could still be significant.

*TreasurySpring’s blogs and commentaries are provided for general information purposes only, and do not constitute legal, investment or other advice.