Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

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, workforce 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 unsupervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clustered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data.