Better functionality will keep devices in service where they already exist and could help drive down costs for new operations in underbanked areas.
It’s not a secret that the current economic climate has put a strain on financial institutions. While many are concerned about a looming recession, increases in bank branch closures pose a major threat to financial inclusion by creating more banking deserts throughout the country and leaving more and more Americans unbanked.
Banking deserts have been increasing for years, but
this trend became more pronounced as a result of the pandemic and changed the ways customers engage with financial institutions. Now, as we face what many are predicting will be a recessionary environment in the second half of 2023, there is growing concern that access to banking services, and cash in general, could be further threatened.
While these trends are concerning,
research commissioned by Diebold Nixdorf sheds light on how automation and technology advances can combat these trends while also address shifting consumer expectations and preferences around financial institutions and ATM functionality. The survey found that cash usage remained strong in 2022, and ATMs continue to drive customer acquisition and retention, with one in five millennials and Gen Xers becoming aware of a financial institution’s product offering or services via the ATM.
This has pushed financial institutions to acquire the tools to adapt and modernize their branches. With a focus on technology-based models, financial institutions are acquiring the tools to not only operate with less staff, but also adapt to evolving consumer preferences, all while closing fewer branches.
Keeping ATMs in service is more important than ever, and advancements in artificial intelligence are unlocking new capabilities in ATM maintenance and upkeep. When ATMs are true Internet-of-things devices, detailed technical data can be continuously collected from sensors and data points and analyzed within cloud-computing platforms with the use of machine learning. This can help them identify and monitor the devices’ lifecycles, and even establish a precise personality profile for every single device operating in the field.
The outcome is that when a device fails, the most likely root cause of the incident can be automatically established within a matter of seconds without the need to send a technician onsite to diagnose the fault. An automated recommendation can be issued about the precise fix, the required level of skills and experience of the technician, the spare parts needed and the time the repair should take. As a result, repairs can be better planned and completed faster.
An AI-driven approach can also enable the shift from a reactive to a predictive service model where incidents can be preempted. By analyzing data patterns, trends, leading indicators and other key data points, impending failures can be identified, so maintenance activities can be scheduled at a time of low customer usage to avoid an unplanned future outage and maximize the uptime of a device.
While increasing ATM uptime is a clear need for financial institutions, the Diebold Nixdorf-commissioned study also illustrates why implementing these services is so vital in today’s economic climate.
ATMs are the second-ranking criteria for consumers when selecting their primary financial institution. Monthly ATM withdrawals increased significantly in 2022, signaling a renewed focus on access to cash. More than 40% of consumers are still using tellers for simple transactions, suggesting there is an opportunity to migrate even more transactions to self-service. This migration will improve operating efficiencies and allow branch staff to focus on higher-value customer interactions and experiences.
At a time when being “always on” is critical to the success of a bank, those financial institutions that smartly apply AI technology to their ATM fleets can ensure they are providing the best possible services to their customers. Better ATM functionality will keep ATMs in service where they already exist and offer an opportunity to drive down costs for new operations in areas where access to cash is critically lacking.
With AI, financial institutions have the technology to be able to better prescribe solutions to problems that threaten to hinder ATM operations. By reducing the rate of ATM failure, they will be better positioned to address the pain points of today’s customers, combat concerns about financial inclusion and provide a service that is built to adapt and improve as economic conditions naturally evolve.
We are setting a new standard for ATM performance with a first-to-industry, scaled, comprehensive and proven solution, which leverages real-time connections with deployed devices across the globe and shifts the paradigm from reactive to truly predictive.
Learn more about DN AllConnectSM Data Engine.
This article was originally published in
BAI Banking Strategies.
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