Planning workforce management for bank operation centers with neural networks
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Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Wseas
Access Rights
info:eu-repo/semantics/openAccess
Abstract
A 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 centers
Description
Keywords
Machine Learning, Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), Time Series Forecasting, Predictive Analytics, Employee Assignment
Journal or Series
Proceedings of the 15th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '16)
