IDEAS home Printed from https://ideas.repec.org/a/igg/jissc0/v5y2014i4p41-57.html
   My bibliography  Save this article

Potential Indicators Based Neural Networks for Cash Forecasting of an ATM

Author

Listed:
  • Partha Sarathi Mishra

    (Department of Computer Applications, North Orissa University, Baripada, India)

  • Satchidananda Dehuri

    (Department of Systems Engineering, Ajou University, Suwon, Republic of Korea)

Abstract

Cash forecasting is one of the important tasks in the domain of computational finance. A number of tools have been developed by various groups of researchers and are being used by banks or corporate to identify future cash needs. However, due to the high degree of non-linearity of the problem and surrounded by many local optimal solutions, this paper propose a multi-layer locally tuned perceptron (MLTP) to forecast the future needs and at the same time reduce the users frustration. It uses a fine tuned MLTP to forecast a daily cash demand of an automated teller machine (ATM). Further, potential indicators are used to making the model robust in terms of its efficiency and accuracy. The accuracy is compared against a traditional time series method. Furthermore, it is validated using the past data collected from the SBI ATM of Bhadrak district of Odisha, India. The performance of the method is encouraging. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.

Suggested Citation

  • Partha Sarathi Mishra & Satchidananda Dehuri, 2014. "Potential Indicators Based Neural Networks for Cash Forecasting of an ATM," International Journal of Information Systems and Social Change (IJISSC), IGI Global, vol. 5(4), pages 41-57, October.
    • Handle: RePEc:igg:jissc0:v:5:y:2014:i:4:p:41-57
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijissc.2014100103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jissc0:v:5:y:2014:i:4:p:41-57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.