Cash accounts for 14% of consumer payments in the United States, more than 80% of consumers used it in the past 30 days, and 90% expect to keep using it, the Federal Reserve said Aug. 4 in its 2026 Diary of Consumer Payment Choice.

That persistence leaves banks locked in an old tradeoff. Either they overstock ATMs with cash and leave capital sitting idle, or they understock them and risk an outage, an extra armored car run or frustrated customers at empty machines.

Artificial intelligence is starting to narrow the tradeoff by treating it as a forecasting problem rather than a guessing game. H2O.ai, an enterprise AI software company, builds cash-demand models for individual ATMs using historical withdrawal patterns, paydays, holidays and regional seasonal trends, achieving forecast accuracy within roughly 15% on average, the company said on its website.

That precision lets a bank stock a machine closer to what it needs on a given day instead of padding every ATM with a buffer sized for the worst case, freeing up cash that would otherwise sit locked in a machine instead of earning a return.

Brink’s takes that forecasting further, combining it with how cash actually gets ordered and delivered. By integrating cash supply, branch inventory forecasting, order optimization and cash monitoring into one system, Brink’s can reduce total cash demand across an entire ATM portfolio by 30% to 40%, the company said on its website. Brink’s analyzes future cash requirements seven to 10 days in advance, giving inventory managers enough lead time to anticipate a spike or dip before it happens.

Source link