It is annoying to find empty ATMs – bring on the smart forecasting cash machines! #AI #AIBanking Click To Tweet
The rise of smart cash machines
These smart ATMs can benefit from the use of a particular type of deep learning method called recurrent neural networks (RNNs). RNNs are specifically designed to handle sequential data, such as speech, text or – importantly in this case – time series.
RNNs are called recurrent because they perform the same task for every element of a sequence, like information about withdrawals from cash machines. The output for each element depends on the computations of its preceding elements.
They are very good at forecasting, especially when demand follows observable patterns. They are able to translate previous events into good forecasts of future demand. They do, however, need a lot of data to perform well: More data leads to better performance and increased levels of accuracy.
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The demand for cash from a cash machine, for example, will depend on its location, local events, and the time of day or week, to name a few. A machine in or near a student union is likely to be heavily used on a Saturday night, and may be empty on Sunday if it cannot be refilled until Monday.
A machine in a shopping street is more likely to be used steadily over the course of the week, with perhaps a peak in demand on Saturday morning. Holiday periods may also affect demand, and will certainly affect replenishment. RNNs use short- and long-term variations in demand to improve the accuracy of their forecasting, and predict the requirements for refilling the machines.
How Will AI Transform Banking?
It may not sound hard to predict that demand for cash will be high on a Saturday night in an area that is full of entertainment venues, or that bank holidays may mean extra cash has to be loaded into cash machines on Friday because the machine will not be refilled on Monday. But flattening out the finer peaks and troughs in demand and supply is more difficult.
Banks want to make their cash work for them, which means getting it into the right place for their customers. Machine learning, and specifically RNNs, offer a way to do that by forecasting demand for cash from particular machines, and ensuring that it is there, ready.
This also benefits the banks because it means their customers are happier: They have access to cash, and they are less likely to switch to another provider. In a world where customers are increasingly demanding, this matters.
This article has been republished with permission from SAS.