Predicting Waiting Time Overflow on Bank Teller Queues

Abstract

This study proposes a predictive model to detect the delay in bank teller queues. Since there are penalties and fines applied to the branches that leave their clients waiting for a long time, detecting these cases as early as possible is essential. Four models were tested: one using a Queuing Theory's formula and the other three using Data Mining algorithms -- Deep Learning (DL), Gradient Boost Machine (GBM), and Random Forest (RF). The results indicated the GBM model as the most efficient, with an accuracy of 97% and a F1-measure of 75%.