Discovering how AI reshapes bank functionalities

AI will create enormous efficiency gains, but it will also erode traditional revenue pools faster than most banks expect.

This is the double problem. Banks need productivity gains in a period of slowing revenue. Yet the same AI systems that lower costs will also empower consumers to move money more intelligently, which cuts straight into the industry’s economics.

Here is the simplest way to think about it.

👉 Banks face two forces at the same time:

How fast they can become fully agentic and radically lower operating costs.

How fast customers adopt AI to manage their financial lives.

McKinsey models nine scenarios based on these two variables. The central scenario, with a 30% probability, is the one regulators, banks and fintech leaders should pay attention to. AI reshapes both operations and consumer behavior at the same time.

This is also the scenario that causes the most disruption.

Agentic AI could drive gross cost reductions of up to 70 percent in certain categories. After rising tech spend, the net effect is still a 15 to 20 percent drop in total costs. Good news, but temporary. Competition usually absorbs gains like this, and customers eventually receive most of the benefit.

The bigger hit comes from customer behavior. Think about deposits. Today, about 23 trillion dollars of the 70 trillion dollars in global consumer deposits sit in checking accounts earning near zero. AI agents change that. Even if customers still approve each transaction manually, agents can surface better yields constantly and move money with almost no friction.

If only 5 to 10 percent of checking balances migrate to top-of-market rates, banks could lose more than 20 percent of total deposit profits. This is one of the clearest examples of what happens when you remove customer inertia from the system.

A more extreme scenario, where consumers fully delegate financial decisions to third-party AI agents, is less likely in the medium term. Regulators would need to allow agents to execute transactions autonomously, and AI systems would need to match senior-level decision making. Both are a stretch today. But disruption does not require full autonomy. Even a semi-autonomous model puts pressure on margins.

Across all scenarios, one thing is consistent. A breakout agentic business model will emerge in the next three to five years. That moment becomes the tipping point.

If banks do not adapt their models, global profit pools could fall by 170 billion dollars over the next decade, a nine percent decline. That would push average returns below the cost of capital.

We are still in the early innings. Banks should not chase AI because of fear of missing out. They should identify the specific workflows where agentic AI drives real earnings impact and build from there.

Insights by McKinsey

Author

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