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An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Renu Bhandari, Jasmeen Gill
10.5120/ijca2016907770

Renu Bhandari and Jasmeen Gill. Article: An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks. International Journal of Computer Applications 133(3):13-16, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Renu Bhandari and Jasmeen Gill},
	title = {Article: An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {3},
	pages = {13-16},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Automatic teller machine (ATM) is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Accurate ATM forecasting for the future is one of the most important attributes to forecast because business sector, daily needs of people are highly largely dependent on this. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. In this research article an integrated BP/GA technique is proposed for accurate ATM forecasting. The results are very encouraging. The comparison of proposed technique with the previous one clarifies that the proposed model outperforms the previous models.

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Keywords

ATM Forecasting, ANN, Back propagation Algorithm, Genetic Algorithms, Hybrid Techniques.