Authors
Abstract
Banks, on the one hand are involved with the challenge of inadequate cash to meet the customers’ needs and on the other hand, are reluctant to increase the costs resulting from the cash excess transfer. As a result, estimating the cash requirements of the bank's branches, according to their daily operations, which is considered as a multivariable system, is one of the most important issues in banking. In this regard, employing data mining, especially clustering methods and neural networks can help to increase the accuracy of estimating the cash required in branches. In this regard, Neural networks are considered significant in terms of flexibility, nonlinearity, greater tolerance to noise and independence from the basic assumptions about the input data.
In the present paper, 20 branches of Tejarat bank have been categorized in similar clusters, during the period 21/04/2014 and 22/09/2014, according to factors such as branch grade, the type of branches in terms of deposit or facility, the number of ATMs, stand-by branches. Then, considering the clustering results and the variables related to the cash of branches such as week days, payment/ deposit subsidy/ deposit interest days, holidays and official events, as well as the amount of cash used in ATMs, the suitable structure for the neural network has been identified to estimate the required cash via the error criteria and the required cash is accordingly estimated for different clusters. The results show that the neural network, considering the clustering results, can estimate the required cash of branches in different clusters with good performance with a mean absolute error of 5%.
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