Serengil, Şefik İlginÖzpınar, Alper2019-08-202019-08-202016https://hdl.handle.net/11467/2884A bank operation center provides a revolutionary efficiency to reduce operational workload of branches. In this way, offering faster, more accurate and high quality service is aimed to increase service quality. Service quality is also based on predicting transactions counts before time to make employee planning properly. In this paper, transactions of bank operation centers are considered as time series problem and a model is proposed for forecasting the transaction counts for different operation types with artificial neural networks. This model was simulated for forecasting Money Order and EFT operations which are the most active transactions of operation centerseninfo:eu-repo/semantics/openAccessMachine LearningArtificial Neural Networks (ANN)Multilayer Perceptron (MLP)Time Series ForecastingPredictive AnalyticsEmployee AssignmentArticle184188