Could AI agents break fractional-reserve banking?

Banks perform a useful trick. They let customers withdraw money from checking and savings accounts at short notice, while using much of that funding to support mortgages and business loans that may not be repaid for years.

The system works because customers do not all ask for their money back at the same time. Most balances are remarkably stable. They move slowly enough that banks can treat a large part of their deposits as dependable funding, even though customers are legally free to withdraw them.

AI agents could make that assumption much less reliable.

The obvious concern is a faster bank run. Apollo economist Torsten Slok recently pointed to an average annual interest rate of 0.1 per cent on US checking-account balances while selected alternatives advertised yields of 3.3 to 5.0 per cent. An AI agent that continually searched for a better return could move money far faster than a human customer normally would.

But a bank run may not be the most important effect. The bigger change could be that banks have to start competing much harder for money they currently receive cheaply and keep for long periods.

The customer may stay while the money leaves

People do not choose bank accounts on interest rates alone.

A checking account may provide payments, cash access, customer service, deposit insurance and the comfort of dealing with a familiar institution. For some customers, those benefits justify earning less interest. That does not mean the customer must keep all their money there.

An AI agent could understand that someone wants to keep US$5,000 in a familiar bank for bills, cash withdrawals and everyday payments, but does not need another US$50,000 sitting in the same account earning very little. The agent could leave the service relationship intact while moving the surplus money somewhere that pays more.

This is important because the bank could keep the customer but lose much of the funding that made the customer profitable.

Large banks benefit from bundling many services together. Yet several parts of that bundle can already be obtained elsewhere. Smaller insured banks can provide the same standard US deposit-insurance protection within the relevant limits. Shared ATM networks can provide cash access without every bank building its own network. Customer service is increasingly automated.

As AI gets cheaper, agents could make it easier to compare the whole package rather than simply compare interest rates. They could ask: How much cash access does this customer actually need? Do they care about branches? What fees would moving money trigger? Is the alternative account insured? How much money should remain instantly available?

Automatic cash management is not new. Wealthfront introduced Autopilot in 2020, allowing customers to move excess cash according to rules they had already set. The difference with a more capable agent is that it could potentially choose between providers as well as execute transfers. The open question is whether that increase in capability is large enough to change consumer behaviour at scale.

Also Read: How a 39-store retail chain in Laos moved from Excel to AI agents

There is already evidence that greater attention changes bank pricing. Research from the Bank for International Settlements finds that US digital banks tend to pay higher deposit rates and adjust them more strongly when central-bank rates change.

Agents could make many more customers behave like highly attentive customers.

Banks would have to start paying for stability

That creates two separate problems for banks.

First, deposits become more expensive. A bank may have to raise its savings rates to stop money leaving.

Second, even after matching a competitor’s rate, the bank has no guarantee that the money will stay. If another bank improves its offer tomorrow, the customer’s agent can move again.

For banks that need reliable funding over longer periods, fixed-term deposits therefore become more valuable. Instead of relying on customers simply leaving money alone, the bank pays them for a contractual commitment to keep it there.

Interestingly, agents could help on this side too. An agent might calculate that a household will not need US$20,000 for the next year and place it into a fixed-term account. So the same technology that makes instant-access deposits less stable could increase the supply of deposits committed for longer periods.

The important change is that banks would increasingly have to pay explicitly for stability rather than receive part of it through customer inertia.

There is also a more extreme risk. Silicon Valley Bank lost US$42 billion in a single business day during its 2023 crisis. That was a confidence-driven run, not an AI event, but it showed how quickly deposits can already move.

If millions of agents use similar decision rules, withdrawals could become more synchronised. A small pricing problem could become a liquidity problem if a bank suddenly had to replace large amounts of funding at much higher cost.

Who actually pays when deposit rates rise?

Initially, the bank does.

If a bank raises the interest paid to depositors, its costs rise. It then has four broad choices: charge borrowers more, reduce its operating costs, accept lower profits, or make fewer loans. In practice, all four could happen at once.

This is why higher returns for savers do not automatically mean equally higher mortgage and business-loan rates.

Banks spend a great deal of money operating branches, call centres, compliance teams, administrative processes and large technology systems. If AI reduces those costs, banks could pay depositors more while spending less elsewhere.

Borrowers may also become more price-sensitive. Their agents could compare loan offers, prepare applications and identify when refinancing makes financial sense. That would make it harder for a bank simply to pass every increase in its funding costs to borrowers.

Also Read: Who referees the AI agents? Enterprises aren’t sure, new report shows

So the extra return paid to savers could come from several places. Borrowers may pay somewhat more. Bank profits may fall. Operating costs may fall. Some loans that no longer make economic sense may disappear.

The result depends heavily on competition.

A lean bank might pay depositors more, charge borrowers no more than today, and remain profitable because its operating costs are much lower. A bank carrying thousands of employees, expensive branches and legacy systems would have a much harder time doing the same.

This does not create specialist finance. It changes the economics of it

Specialist lenders and specialist funding providers already exist at scale.

US finance companies owned or managed about US$1.95 trillion of loans and leases in July 2026. In the UK, Hampshire Trust Bank already takes deposits while financing independent lending companies. Nonbank mortgage companies commonly borrow from banks, originate mortgages and then sell those loans to investors.

AI agents would not invent this structure. What they could change is its cost and competitive reach.

A specialist lender could automate more of its administration, reach customers more cheaply and operate profitably in smaller niches. At the same time, customers’ agents could make those specialists easier to find.

But the large banks would not simply disappear. Specialist lenders often depend on banks for funding. If bank funding becomes more expensive, some of that cost flows through to them too. Large banks also retain advantages in capital, regulation, liquidity and access to emergency funding.

Also Read: AI agents broke into 100 firms for pocket change. SEA’s online shops fit the target profile

So the likely result is not a clean transfer of business from large banks to thousands of tiny replacements. It is more likely to be a squeeze on the margins throughout the system.

Savers may capture more of the available return. Borrowers may gain from better comparison. Banks may retain less income between the two. Some of the value may move to specialist lenders, funding providers or the agent platforms themselves.

Fractional-reserve banking probably survives, but in a different form

AI agents do not automatically end fractional-reserve banking.

Banks can respond by paying more for deposits, attracting more fixed-term money, holding more liquid assets and reducing their own operating costs.

The deeper change would occur only if long-term lending became much less dependent on deposits that customers can withdraw immediately and much more dependent on money that savers and investors have committed for longer periods.

That would be more than a change in which company provides the mortgage or savings account. It would change the type of money supporting the lending.

The first test is much simpler. Once reliable agents can move money easily between banks, do large banks continue to hold huge amounts of cheap, stable deposits?

If they do, the existing model is more durable than this thesis suggests.

If they do not, one of the oldest economic advantages in banking begins to disappear.

Read “Abundance Economics: The Economic Mechanisms of the AI Age” here, or find more on the research framework behind it here.

—

The post Could AI agents break fractional-reserve banking? appeared first on e27.