It’s been a while since ATMs have come into existence. Despite credit cards and internet banking gaining popularity, those in need of cash continue to make use of ATMs.
Much like all other aspects of investment banking and money transactions, ATMs have also undergone notable innovations.
One issue that has always plagued financial institutions is how to determine the amount of money held by an ATM at a time. The optimization of cash management has always been a relevant issue. Decisions regarding this were usually taken based on reports by the staff.
There were often predictable trends such as the hike in withdrawals before a holiday like Christmas, or a reduced number of withdrawals in ATMs in the proximity of shopping malls during weekdays.
Banking officials are familiar with the struggle of making sure that the ATMs are carrying enough money and are not overfilled with unused funds. Lack of proper optimization can also affect customers when the ATMs run out of money or when they don’t perform efficiently during a hectic day.
Since ATMs work 24×7, there is no way for the cash levels to be continuously monitored and maintained. Different ATMs by the same bank will also require different amounts of cash. Some banks often keep a significantly larger quantity than is necessary, such as 40% more, in their ATMs. This overfilling puts pressure on the institution to maintain higher working capital than necessary and prevents them from utilizing the unused money for profits.
As ATM networks keep expanding, it is essential to remove the minimize the human element in determining the required cash and optimizing it for maximum profit.
Machine learning for cash optimization
Banks all around the world have come to realize the necessity of using artificial intelligence in their ATM software to determine the best cash level. Cash optimization systems use data collected from people’s withdrawal behaviours to create withdrawal forecasts. This software either study the patterns in a limited period, such as a single day or over several weeks.
The software can also be equipped to predict any changes or errors. It can be used to determine the cash required, and the dates of refill and deposits.
Using Artificial Neural Networks (ANN) in Machine Learning
ANNs are highly specialized function approximators commonly used in financial sectors. They help figure out patterns which help in cash budgeting. ANN-based systems will help efficiently predict the demand for cash at every single ATM. The ANN uses data from a particular day to figure out the trends of the next day. The cost of cash refilling and maintenance are also pumped into the ANN to figure out a model that will minimize the bank’s expenditure.
These ANNs require highly trained data scientists to help create results and forecasts. ANNs can even help determine the number of notes of each denomination needed for an ATM. The scope for ANNs in the ATM industry is vast since they can help predict more than just cash levels required. Scientists can program ANNs to determine the best time of the day for refills, the best location for an ATM and even the most efficient route to refill multiple ATMs at once.
ATMs no longer have to depend on human predictions and analytics to maintain the proper levels of cash. Banks can fine-tune Cash Optimization in ATM networks with the use of machine learning and Artificial Neural Networks. These will minimize the hassles caused by not having enough money in the machine or wasting large amounts in an ATM. These methods can help make ATMs more efficient, banks more profitable, and give the customers a better experience.
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