E-Banking and Emerging Multidisciplinary Processes: Social, Economical and Organizational Models (Premier Reference Source)
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The innovative utilization of the Internet and other information and communication technologies in the banking sector has created somewhat of an e-banking phenomenon.
E-Banking and Emerging Multidisciplinary Processes: Social, Economical and Organizational Models advances the knowledge and practice of all facets of electronic banking. This cutting edge publication emphasizes emerging e-banking theories, technologies, strategies, and challenges to stimulate and disseminate information to research, business, and banking communities. It develops a comprehensive framework for e-banking through a multidisciplinary approach, while taking into account the implications it has on traditional banks, businesses, and economies.
accessible from anywhere in the world by unknown parties, with routing of messages through unknown locations and via fast evolving wireless devices. Therefore, it significantly magnifies the importance of security controls, customer authentication techniques, data protection, audit trail procedures, and customer privacy standards. The above risk challenges could also exacerbate money laundering activities. For example, the rolling out of new business in a very short time frame could lead to banks
Knowledge Management Integrated – Concepts and Practice (pp. 187–211). Australia: Heidelberg Press. Al-Shammari, M. (2008). Toward a Knowledge Management Strategic Framework in Arab Region. International Journal of Knowledge Management, 4(3). Alavi, M & Leidner DE (1999). Knowledge Management systems: Issues, challenges, and benefits, Communication of the Association of Information Systems, 1, Article 7, Feb 1999. American Productivity & Quality Center (APQC). (1997). Knowledge Management:
Lee, M. Ch. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8, 130–141. doi:10.1016/j.elerap.2008.11.006 Ho, Ch. T. B., & Wu, D. D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research, 36, 1835–1842. doi:10.1016/j.cor.2008.05.008 Liao, S., Shao, Y. P., Wang, H., & Chen, A.
fuzzy algebra that produces a ranking of the business units within a financial institution. Both linear and non-linear models have been developed for the measurement of operational risk. Linear models include regression models, discriminant analysis, etc. The non-linear models, based on artificial intelligence, try to capture the non-linearities in operational risk. Neural networks are an alternative to non-parametric regressions. Bayesian belief networks have attracted much attention recently as
a possible solution to the problems of decision support under uncertainty. Bayesian networks provide a lot of benefits for data analysis. The first is due to the fact that the model encodes dependencies among all variables and it also handles missing data. Further, they can be used to learn causal relationships and hence used to gain an understanding of problem domains and to predict the consequences of intervention. Data mining can be extremely useful in estimating hidden correlations and