2016 Putting ATM Cash Requirements into Context, ANN relation to socioeconomic events and Variety

Authors

George Vranopoulos, eProject
Athanasios Triantafyllidis, Ph.D.
, Deree - The American College of Greece
Marianthi Yiannopoulou, eProject

Citation

Vranopoulos, G., Triantafyllidis, A., & Yiannopoulou, M. (2016). Putting ATM Cash Requirements into Context, ANN relation to socioeconomic events and Variety. In M. Yiannopoulou (Ed.), BEFB 2016: International Congress on Banking, Finance, and Business ISSN 2412-4044 (pp. 526–545). Sapporo, Japan: BEFB. Retrieved from http://iainst.org/befb/

Type

Peer Reviewed, Presented & Published

Venue

BEFB 2016: International Congress on Banking, Finance, and Business, Sapporo, Japan

Abstract

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.

Keywords: ATM, ANN, Neural Networks, Big Data Variety, Cash Demand Forecasting

 

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