Identification of Unexpected Behavior of an Automatic Teller Machine Using Principal Component Analysis Models
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.
KeywordsAutomatic teller machine principal component analysis ATM network supervision unexpected behavior
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