Artificial Neural Network relation to socioeconomic events and Variety.
G. E. Vranopoulos, A. Triantafyllidis, M. Yiannopoulou (2016) Putting ATM Cash Requirements into Context: Artificial Neural Network relation to socioeconomic events and Variety, Proceedings of 2015 International Congress on Banking, Economics, Finance, and Business, pp. 526–545.
BEFB 2016, International Congress on Banking, Economics, Finance, and Business
Dates: June 24-26, 2016
Location: Sapporo, Japan
Status: Paper Peer Reviewed & Presented
Developing a cash demand forecasting model for ATMs’ network is a challenging task since there are substantial fluctuations over time and depends on the location of the ATM. Variety is one of the principal V’s of Big Data, having context being one of the most important aspects. In this paper the ATM cash withdrawals datasets have been “placed into context” by incorporating socioeconomic datasets. This contextual enhancement improves the effectiveness of the ANN model devised.