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Identification of Unexpected Behavior of an Automatic Teller Machine Using Principal Component Analysis Models

  • Rimvydas Simutis
  • Darius Dilijonas
  • Lidija Bastina
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 37)

Abstract

Early detection of the unexpected behavior of the automatic teller machine (ATM) is crucial for efficient functioning of ATM networks. Because of the high service costs it is very expensive to employ human operators to supervise all ATMs in an ATM network. This paper proposes an automatic identification procedure based on PCA models to supervise continually the ATM networks. This automatic procedure allows detecting the unexpected behavior of the specific automatic teller machine in an ATM network. The proposed procedure has been tested using simulations studies and real experimental data. The simulation results and the first real tests show the efficiency of the proposed procedure. Currently the proposed identification procedure is being implemented in professional software for supervision and control of ATM networks.

Keywords

Automatic teller machine principal component analysis ATM network supervision unexpected behavior 

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References

  1. 1.
    Snellman, H., Viren, M.: ATM networks and cash usage. Research Discussion Papers. Bank of Finland Nr. 21, 1–33 (2006)Google Scholar
  2. 2.
    Bounie, D., Francois, A.: Cash, Check or Bank Card: The effect of transaction characteristics on the sue of payment instruments. Telecom Paris, Working papers in Economics and Social Sciences, ESS-06-05 (2006)Google Scholar
  3. 3.
    Simutis, R., Dilijonas, D., Bastina, L., Friman, J., Drobinov, P.: Optimization of cash management for ATM network. Informacinės technologijos ir valdymas = Information technology and control 36(1A), 117–121 (2007)Google Scholar
  4. 4.
    Simutis, R., Dilijonas, D., Bastina, L.: Cash demand forecasting for ATM using neural networks and support vector regression algorithms. In: EurOPT 2008: the 20th International Conference Euro Mini Conference on Continuous Optimization and Knowledge-Based Technologies, Neringa, Lithuania, Vilnius, May 20-23, pp. 416–421 (2008) (selected papers), ISBN 978-9955-28-283-9Google Scholar
  5. 5.
    Roffel, B., Betlem, B.: Process Dynamics and Control. John Wiley & Sons, Ltd, Chichester (2006)Google Scholar
  6. 6.
    Jackson, J.E.: A User’s Guide to Principal Components. John Wiley, Chichester (2003)Google Scholar
  7. 7.
    Nomikos, P., MacGregor, J.: Multivariate SPC Charts for Monitoring Batch Processes. Technometrics 37, 41–59 (1995)CrossRefGoogle Scholar
  8. 8.
    Qin, S.J.: Statistical Process Monitoring: Basics and Beyond. Journal of Chemometrics 17, 480–502 (2003)CrossRefGoogle Scholar
  9. 9.
    Friedman, J.H.: Exploratory projection pursuit. J. Am. Statist. Assoc. 82, 249–266 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rimvydas Simutis
    • 1
  • Darius Dilijonas
    • 1
  • Lidija Bastina
    • 2
  1. 1.Kaunas Faculty of HumanitiesVilnius UniversityKaunasLithuania
  2. 2.JSC Penkių kontinentų bankinės technologijosVilniusLithuania

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