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January 14, 2020 | by Diebold Nixdorf
On this podcast we discuss how to transform from traditional sourcing to an "as a service" economy. Today's consumers expect "anytime, anywhere" access to the self-service channel, leaving financial institutions with the challenge of optimizing availability without driving up costs.
November 14, 2019 | by Diebold Nixdorf
DN Series was designed to combat security threat vectors with industry-leading innovations that will reduce your ATM fleet’s overall security risk.
April 28, 2019 | by David Phister
While the industry has been focused on preventing sophisticated digital attacks, criminals have reverted back to more low-tech attack methods to access cash—the latest being Transaction Reversal Fraud (TRF). Here's how you can combat it.
December 10, 2018 | by Marcia Hirata
Do you view your ATM as part of your digital network? At its heart, the ATM is a computer that’s always on, and always connected. Yet it’s so physical in nature—a big terminal that spits out physical currency—it’s easy to think of it as only a physical touchpoint.
August 21, 2018 | by Diebold Nixdorf
In a world where financial institutions – from banks, to niche service providers – are ramping up the pace of innovation by launching new services and introducing new ways of engaging with their customers, the issue of security (and of fraud in particular) has never been such a hot topic.
August 17, 2018 | by David Phister
We've been a trusted security partner for over 160 years. We want to ensure you have the insights, tools and solutions you need to secure your assets, your data and your customers’ trust both today and tomorrow.
July 12, 2018 | by B. Scott Harroff
You might have seen some of the headlines earlier this year about “jackpotting.” It’s an ATM hacking scheme that prompts ATMs to dispense cash as if they were a Vegas slot machine.
March 19, 2018 | by Diebold Nixdorf
Our latest blog explores how we’re using patented machine learning algorithms based on more than 30 years of research work to deduce previously unknown fraud patterns by looking at highly complex Big Data sets in near real-time.