Gaby Alexander
The world has been gradually shifting online in the past couple of decades. In 2020, this shift was inarguably accelerated. Many activities no one imagined would move online, were suddenly carried out in digital platforms and quickly accepted all around the world.
Those who strongly opposed carrying out monetary transactions online suddenly found themselves left with no other alternative. Soon enough, online transactions became the norm, mostly since branches of many banks stayed closed or opened only for a few hours. Those who preferred not to risk their lives chose to stay home and carry out their tasks digitally.
Despite these massive shifts online, banks continue to use a significant percentage of their budgets to provide cash to the customers via ATMs. There is a good reason for that since many who were anxious about the future took to the ATMs to withdraw considerably larger amounts of cash. They did not use it for any immediate purposes but wanted to save it for any future emergencies. This meant that even though the number of people who frequented ATMs might have reduced slightly, those who withdrew cash withdrew a lot more than before.
Challenges and Solutions
Many banks have partnered with cash management services that provide them with advanced forecast systems that accurately predict the cash requirements of each machine installed in every location. This is carried out via machine learning by analyzing massive amounts of historical data. When the pandemic emerged, people’s withdrawal patterns changed considerably. They were no longer following the trends from a few months ago. This meant that systems that analyzed historical data could no longer provide accurate insights. The extreme volatility in withdrawal patterns that banks witnessed during the pandemic prompted them to consider other alternatives to gather data and not rely on the forecast systems.
Cash management services also kept track of the money present in the ATMs, the branches and what was in transit. But often, money was moved to the bank’s own vaults and vendor vaults, and there wasn’t a method to correctly measure the entire amount. Working with vendor vaults and acquiring the data of the cash levels with them often proved to be difficult for these services. Many banks also did not keep close track of their money that was distributed among various locations.
With customer satisfaction in mind, during massive events, banks often make sure that their machines are filled with significant amounts of cash so as to not let any customer return empty-handed. In places that were hit hard economically, banks had to really consider the amounts they deposit in their machines. While the forecast systems did a pretty good job of predicting cash requirements even during massive changes, they did not take into account cash that was present in vaults and branches. It is imperative to optimize cash levels in these other locations, especially during this time. Such optimization will prove essential to cutting down costs while maintaining customer satisfaction.
Forecast systems that consider the bank’s money distributed among various locations including ATMs, branches, vaults, etc. will turn out to be a game-changer for optimizing cash levels and expenditure.
Banks often carry immense amounts of customer data, a lot of which is not necessary for any purposes. Similarly, they also have large amounts of unused data regarding cash transactions, transport facilities, etc. With the availability of more significant amounts of real-time data, banks can stay informed about the money that is currently in transit, how much is present in the ATMs and prevent overfilling or unexpectedly running out of cash. Real-time data will also help the cash forecast system understand sudden events and develop accurate predictions and reports. The system can also make use of real-time data to discover the best locations where the bank can deploy a new ATM and analyze which of its machines it should close or keep open to minimize expenses. These advanced systems fed with data will also enable banks to understand where their customers will go if they closed particular ATMs or branches.
Banks often tend to fill more money than necessary in their machines because they are unsure about how much of it will be leftover, and they must ensure customer satisfaction at all costs. But with the help of real-time data from their own locations and vendor-vaults, they can be much more confident about sticking to a prescribed level of cash in their ATMs while being sure that their customers will have money to withdraw whenever they visit those locations. The banks will also be able to lower the amounts of the bulk cash they order since the levels have been set to a specific quantity at all these sites.
Quality real-time data can be used for much more than efficient cash forecast systems. It can help with revising CIT agreements and other processes involved in the cash flow. The data collected in real-time can be converted into invaluable information that helps with the bank’s growth and ensures steady profits.
Summing Up
Banks and ATM services have to develop measures that safeguard financial institutions against volatilities that occur due to unpredictable emergencies. These systems should be able to sustain banks throughout periods of uncertainty and bring it back in full strength once an opening arises. Currently, banks face several challenges regarding cash optimization during the pandemic, and simple forecast systems have turned out not to provide the necessary tools to survive this period efficiently.
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