ATM maker Diebold is using Microsoft’s Azure machine learning platform and Cortana Analytics Suite to monitor, predict and address issues in its ATM network.
Diebold’s senior director of new technology incubation James Meek said that the goal of 100 percent uptime for the company’s vast network of ATMs would only be possible through data and predictive analytics.
“We can gather more than 250 data points from a single module on an ATM – and that ATM might have 10-15 modules operating at a given time,” Meek said.
“That means there are potentially thousands of data points we can pull into advanced algorithms to create real-time fleet-visualisation tools for financial institutions.”
Meek said that, armed with this aggregated data, a “health score” can be generated that can provide information such as the probability that an ATM will fail in the next 1000 note transfers, or the overall health score rankings for a network of thousands of ATMs.
Diebold said it is currently in the pilot stage of implementing the service with three major international banks.
“Our collaboration with Microsoft is just one way we’re working on new solutions that analyse and shape retail banking data,” Meeks said.
“It’s through multi-partner collaborations like these that we can figure out the right questions to ask, in ways that are fundamentally different than they’ve ever been asked before.”