Workforce optimization for bank operation centers: a machine learning approach
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Dosyalar
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ijimai
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. Inherently, workload volume of money transfer transactions changes dramatically in hours. Therefore, work-force should be planned instantly or early to save labor force and increase operational efficiency. This paper introduces a hybrid multi stage approach for workforce planning in bank operation centers by the application of supervised and unsu-pervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clus-tered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Forecasting, Machine Learning, Predictive Models, Time Series Analysis
Kaynak
International Journal of Interactive Multimedia and Artificial Intelligence
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
4
Sayı
6
