Financial behemoth JPMorgan launched an AI tool called Cash Flow Intelligence for its corporate customers last year. The company said this proprietary AI algorithm — which analyzes cash flows and creates a custom forecast in seconds — has cut human manual work by upwards of 90%.
“What used to take hours now takes a few minutes,” the bank claimed in a video about the tool.
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Cash Flow Intelligence is free and has seen “tremendous” interest from clients, Tony Wimmer, head of data and analytics at JPMorgan’s wholesale payments, explained. In turn, JPMorgan is now thinking about charging customers in the future.
“We are going to keep investing into this solution because we see that we’re starting to really crack this workflow,” Wimmer said.
Technology is only going to improve, so understanding how AI is infiltrating and influencing your daily life is important. The scariest notion people often worry about is AI finding itself in the wrong human hands, as sometimes bad people do bad things with power and access to your banking information.
However, as far as JPMorgan’s Cash Flow Intelligence, you can probably rest easy, Here are a few key takeaways:
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Simply put, JPMorgan’s AI algorithm analyzes vast amounts of financial data, identifies patterns and creates custom cash flow forecasts in a matter of seconds.
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The tool offers the benefit of reduced manual labor as it has been instrumental in doing just that for many corporate customers to the tune of a 90% reduction in manual labor in some areas.
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It provides more accurate and timely cash flow forecasts compared to traditional methods, as well as streamlines efficiency with the cash flow forecasting process.
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Phil Siegel, founder of the AI nonprofit organization at CAPTRS, said that this was an interesting application of AI, albeit “a bit of a black box.”
“Is the AI pattern recognizing from other clients or is it more learning about specific verticals? Also is the application taking income statement data and generating a cash flow model or from the general ledger?” he asked. “In any case, all models like this can both make a worker more productive but also require a human to check the work. Specifics about a company and unique expenditures require people to be involved before approving the output.”
