The Improvement of Forecasting ATMs Cash Demand of Iran Banking Network Using Convolutional Neural Network

Abstract

One of the problems related to the banking system is Automated Teller Machine (ATM) cash demand forecasting. If an ATM faces a shortage of cash, it will face the decline of bank popularity and in turn will have some costs and the bank will encounter decreasing customers use of these systems. On the other hand, if the bank faces cash trapping at an ATM, regarding inflation in Iran, cash trapping and the lack of using it will have a negative impact on bank profitability. The aim of this study is to predict accurately to eliminate the posed double costs. Since the information related to the amount of cash is daily, each ATM will have a behavior as time series and also because the aim of this study is to predict the demand for cash from the 1056 ATMs, we are facing data from the type of panel. The methods that are used for forecasting ATM cash demand in this research include: forecasting by statistical method, artificial neural network intelligent method, Support vector machine and Convolutional neural network. We will compare the results of these methods and show that intelligent methods in comparison with statistical analysis have higher accuracy.