Efficient ATM cash forecasting not only improves the customer experience but also lowers the financial costs of the ATMs by avoiding unused stocked cash. Even though ATM forecasting is a challenging task with big unpredictability of withdrawals, still it’s a profitable avenue with massive potential due to the volume of machines; across the world, there are over 3.5 million ATMs used.
This is one reason why all major banks across the world are investing in ATM cash forecasting technology to improve their efficiency and enhance customer service. The technology can help banks maintain optimum cash stock in the ATMs, thus not losing money with large unused cash stock or losing reputation with low cash. The technology required the development of complex algorithms that can forecast the Cash demand for the ATMs using past data. A robust ATM cash forecast system can assist banks in improving the cash assets while reducing the operational costs of the machines.
Past is the key to the future
Traditionally, the cash management for ATMs is performed manually, mostly relying on individual experience and corporate policies. However, today the financial institutions have had a significant volume of historical cash demand records for the ATM transactions, coupled with the advancement of the AI and Machine Learning technology to create complex and advance cash forecasting algorithms that can identify and address requirements of the cash demands on ATMs.
An efficient and effective ATM cash forecaster model is the preliminary requirement for efficient cash management at ATMs. The models that are being developed across the world are leveraging machine learning algorithms. Since there is a large volume of historical data available with financial institutions for the cash demands at ATMs, these datasets are being used to train the machine learning algorithms, which then gives off accurate ATM cash forecasting.
What makes it difficult than some of the other applications of machine learning is the unpredictability of cash demands. For instance, there may be random large cash transactions that may stand well-above the threshold level, thus the algorithms need to account for such high or low anomalies when making the ATM cash forecasting.
Harnessing artificial neural networks
Artificial Neural Networks (ANNs) are increasingly becoming an important forecasting component for Artificial Intelligence (AI) technology. The ANNs are highly flexible approximates, which can be used in various AI and machine learning applications like image processing, object identification, classification, time-series forecasting, and much more. The Artificial Neural Networks can identify the nonlinear relationships in the data, which makes them extremely useful to predict the cash demand at ATMs.
The key for successful implementation of ANNs for cash forecasting is the configuration of the internal parameters, as well as the structure, size, and quality of the data used for training purposes. When applied specifically in cash of banks requirements, the ANNs can help them estimate the optimum amount of cash required at each ATMs by analyzing the historical data, thereby largely improving the efficiency of the banks to stock required levels of stock for customers, as well as, saving them the cost for unused stocks in the machines as well.
Apart from the forecasting, the cash requirement in ATMs, machine learning, and artificial neural networks (ANNs) can also help banks to improve their operational efficiencies by determining the best schedules for ATM replenishments, as well as, by predicting the optimal time to stock the machines.
Cash forecasting has become indispensable for banks to efficiently manage their ATM network. The cash management solutions including the ATM cash forecasting is one of the many technologies that are optimizing the effectiveness and efficiency of banks to keep an optimum level of cash in the machines. The advancement in machine learning and artificial intelligence technologies are only adding to the development of more integrated and highly accurate algorithms that can be used to predict cash demands for ATMs.
Already, various leading tech companies like Folio3 and others are developing customized ATM cash forecasting systems that are specifically developed to meet the unique needs and requirements of individual banks, thus even further improving the utility of the cash management system for financial institutions.