ATM Forecasting using Optimized Artificial Neural Networks

Abstract

Abstract: Running out of money at an ATM or other place results in higher costs for unscheduled currency supplies and less income from lost surcharge fees. However, banks are unable to invest this non-earning assets to produce interest revenue due to oversupply of currency. ATM forecasting automation is therefore urgently needed. Artificial neural networks are utilized as a forecasting method in this study because they function well for automated prediction tasks. The most significant algorithm for neural network training is propagation in reverse. However, it is prone to become stuck in local minima, which might result in incorrect answers. Thereby, in order to hybridize with artificial neural networks, a few global search and optimization strategies were needed. Genetic algorithms that mimic the idea of natural growth are one such method. Therefore, a hybrid intelligent system that combines genetic algorithms and neural networks made up of neurons is presented for ATM forecasting in this study. The suggested mixed approach functioned better than the current back propagation-based system, according to the results