Time-Critical Decision Making in Banking Transaction: Using Bayesian Algorithm
Keywords:
Time-critical Decision Making, Bayesian Algorithm.Abstract
This research paper treat the basic perception of time-critical decision making in Banking transaction for bank customers, as it create an awareness approach to ease time spent in performing day-to-day transaction at the bank that may involve either withdrawal with or without the use ATM or deposit as the case may be. Most a time at the bank, people spent a lot of time in trying to either withdraw money or deposit money at the various bank branches all over Nigeria. This is as a result of lack of proper time-critical decision support for people to understand what is happening at bank branches. Waste of man hour at the bank may arise due to location of the banks, day to day banking transaction involving withdrawal or deposit done on Friday. This paper is intended to create a time-critical decision support in the banking transaction to assist individual or groups to understand and be aware of certain situations that will assist banking transaction in any of the bank branches in Nigeria. It uses the Bayesian algorithm which is implemented in MATLAB version 7.7.0 to determine the bank branch to carry out transaction. Choice of location for transaction is dependence on the highest posterior probability calculated for a given predictor in predicting the situation and a graph, showing the plotted points.
References
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[2] Zwass, V. (1996). Electronic Commerce: Structures and Issues, International Journal of Electronic Commerce, 1(1), pp. 3-23.
[3] Lallmahamood, M. (2007). An examination of individual’s perceived security and privacy of the internet in Malaysiaand the influence of this on their intention to use E-Commerce: Using an extension of the technology acceptancemodel, Journal of Internet Banking and Commerce, 12 (3), pp. 1-26.
[4] Chau, P.Y.K., and Lai, V.S.K. (2003). An empirical investigation of the determinants of user acceptance of InternetBanking, Journal of Organizational Computing and Electronic Commerce, 13 (2), pp. 123-145.
[5] Rashid, M.A., and AI-Qirim, N.A. (2001). E-Commerce technology adoption framework by New Zealand smallto medium size enterprises, Research Letters Information Mathematical Science, 2 (1), pp. 63-70.
[6] Tan, M., and Teo, T. (2000). Factors influencing the adoption of Internet Banking, Journal of the Association forInformation System, 1 (5), pp. 1-42.
[7] Frust, K., Lang, W.W., and Nolle, D.E. (2000). Internet banking: Developments and Prospects, Economic and Policy Analysis Working Paper 2000-9, September.
[8] Mukherjee, A., and Nath, P. (2003). A model trust in online relationship banking, The International Journal of Bank Marketing, 21 (1), pp. 5-15.
[9] Hernando, I., and Nieto, M.J. (2007). Is the internet delivery channel changing banks’ performance? The Case ofSpanish Banks, Journal of Banking and Finance, 31 (4), pp. 1083-1099.
[10] Chung, W., and Paynter, J. (2002). An Evaluation of Internet banking in New Zealand. Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 2410-2419.
[11] Jayawardhena, C., and Foley, P. (2000). Changes in the banking sector – the case of internet banking in the UK, Internet Research, 10 (1), pp. 19-30.
[12] Giannakoudi, S. (1999). Internet banking: the digital voyage of banking and money in cyberspace, Information and Communications Technology Law, 8 (3), pp. 205-243.
[13] Sayar, C., and Wolfe, S. (2007). Internet banking market performance: Turkey versus the UK, The International Journal of Bank Marketing, 25 (3), pp. 122-141.
[14] Polasik, M., and Wisniewski, T.P. (2009). Empirical analysis of internet banking adoption in Poland, The International Journal of Bank Marketing, 27 (1), pp. 32-52.
[15] Palmer, J.W. (2002). Web site usability, design, and performance metrics, Information Systems Research, 13 (2),pp. 151-167.
[16] Mavri, M., and Ioannou, G. (2006). Consumers’ perspectives on online banking services, International Journal ofConsumer Studies, 30 (6), pp. 552-560
[17] Eriksson, K., and Nilsson, D. (2007). Determinants of the continued use of self-service technology: The case of Internet banking, Technovation, 27 (4), pp. 159-167.
[18] Kim, B.M., Widdows, R., and Yilmazer, T. (2005). The Determinants of Consumers’ Adoption of Internet Banking. Retrieved June 20, 2009 from http://www.bos.frb.org/news/conf/payments2005/yilmazer.pdf.
[19] Michael D. Clemes, Christopher Gan and Junhua Du (2012), Banks and Bank Systems, Vol. 7, Issue 3, pp. 34-36
[20] McGrayne, Sharon Bertsch. (2011). The Theory That Would Not Die, p. 10., p. 10, at Google Book
[21] Stigler, Stephen M. (1986) The history of statistics. Harvard University press. Chapter 3.
[22] Fienberg, Stephen. E. (2006) When did Bayesian Inference become "Bayesian"? Bayesian Analysis, 1 (1), 1–40. See page 5.
[23] H. Smith.(2011) Violence on campus: Practical recommendations for legal educators. Retrieved from University of Tennessee: http://works.bepress.com/sandra_thomas/16/, 2011.
[24] Bernardo, J.M. (2005). Reference analysis, Handbook of statistics, 25, 17-20
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[38] Stigler, Stephen M. (1986) The history of statistics., Harvard University press. pp. 97–98, 131
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Published
2016-08-04
How to Cite
Baah, B., & Nwiabu, N. (2016). Time-Critical Decision Making in Banking Transaction: Using Bayesian Algorithm. American Scientific Research Journal for Engineering, Technology, and Sciences, 23(1), 29–40. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/1683
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