Agentic AI, systems that can perform tasks and solve issues with minimal human intervention, are set to disrupt the economic foundations for finance.
According to a new report by McKinsey, this technology is poised to affect billions in revenue and challenge the business models and revenue at banks, small and medium-sized enterprises (SMEs), credit-card companies, and others. This disruption will stem largely from making traditionally passive aspects of banking programmable and dynamic.
Deposits will become fluid
The report, released in August, highlights two especially vulnerable revenue streams in banking: deposits and credit cards. These areas rely heavily on customer inertia and brand loyalty, making them especially vulnerable to agentic AI.
Deposits, including consumer checking and SME operating accounts, currently power bank profitability. Globally, net income interest income accounts for roughly 30% of retail banking profits.
Most consumers don’t notice the interest rate they are receiving, or they lack the time, tools, and incentive to maximize interest returns on their deposits. Instead, they prioritize convenience, focusing on areas such as waived fees, ATM networks, and integrated services like bill payments and wealth portals.
Agentic AI systems have the potential to reverse this logic. These agents can monitor balances in real time, compare returns across institutions, sweep idle cash into higher-yield accounts, and then sweep cash back to a checking account in time for bills. This shift would redirect part of the spreads once captured by banks back to account holders.
SMEs are already leveraging API-driven treasury automation to optimize cash and foreign exchange (FX) in real time. For example, several businesses are using cash management platforms that automate daily reporting, forecasting, sweep operations, and even FX hedging. Agentic AI would take this further, integrating these capabilities into continuous, preference-driven treasury operations.
The stakes here are high. Each year, banks in Europe earn over US$100 billion from deposits. If just 10% to 20% of people used AI agents that automatically move their cash into higher-paying accounts, constantly shifting their money to get the best deal, banks’ profits from deposits could shrink by about 0.3-0.5%, McKinsey estimates, posing a clear threat to lenders, it warns.
Optimizing rewards and spending on credit cards
Similarly, credit cards are another major source of revenue banks, generating US$234 billion in 2024. These revenues come from a blend of interest income from customers who carry a balance, interchange fees, annual and penalty fees, and unredeemed rewards.
Yet, many consumers fail to maximize rewards. A 2024 survey conducted by Bankrate in the US found that almost a quarter of rewards cardholders (23%) didn’t redeem any rewards in the prior year. According to the US Consumer Financial Protection Bureau, about 3-5% of earned rewards points disappear each year through either account closure or expiration.
AI agents are poised to change this by automatically directing spending to the best card in real time. These systems could also roll balances to another card before promotional rates expire, and apply for new cards with better offers.
Some of this automation is already happening. Klarna’s Money Story feature, for example, uses data from all spending with the payment services company, such as purchases made with the Klarna App, the Klarna Card and at partnered retailers’ checkouts, to offer a snapshot into a customer’s spending patterns, and help them better budget.
Another example is Apple’s Daily Cash instant cashback program, which allows customers to earn when using the Apple Card. If customers choose to, these rewards can be automatically sent to a high-yield savings account.
Adoption of agentic AI on the rise
Agentic AI are AI systems designed to act with autonomy, making decisions and taking actions without constant human oversight in pursuit of defined outcomes. Unlike other AI models, agentic AI can plan, adapt, and coordinate across tasks, giving these systems more initiative and independence in complex environments.
In banking, real-world agentic AI applications are still in nearly stages but adoption is accelerating. According to 2024 and 2025 studies by the International Data Corporation (IDC), 78% of banks are actively exploring agentic AI: 38% are already investing with a defined spending plan for the technology, while 40% already tested some agent solutions but have no spending plan yet.
Several banks are already employing agentic AI. At Bank of New York Mellon (BNY), for example, AI agents are working autonomously in areas like coding and payment instruction validation. Meanwhile, payment firms including Mastercard, PayPal and Visa, are experimenting with “agentic commerce”, where AI agents autonomously execute transactions on behalf of consumers.
In Asia, banks see the greatest potential in improving customer experience (39%), operational efficiency (36%), data-based decision making (28%), and task automation (28%), according to IDC research.
Research firm Gartner forecasts that by 2028, at least 15% of everyday workplace decisions will be made autonomously through agentic AI, up from none in 2024. By then, 33% of enterprise software applications will include agentic AI features, compared to fewer than 1% in 2024.
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