Decision Support System in the E-banking Sector
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Published: Wed, 14 Mar 2018
An analysis on the effective use of the Decision Support System in Technology of the E-Banking Sector in general The use of electronic banking has undergone tremendous growth over the years. This has been facilitated by the increased interest in information and technology. As a result, many companies and organizations have embraced this trend due to the increasing trend towards globalization. However, due to the immensity in the information reservoirs across the banking sector facilitated by increased customer base, banks have seriously embraced the management information system with high levels of programming being experienced across all the departments of their operations. Of overwhelming importance, has been the quest for market sustainability and the drive to manage the stiff competition across all financial sectors. Many organizations have over the years experienced customer loss to their competitors as a result of many factors, such as speed of service delivery, confidentiality of client information, and quality of service provided. This has called for the use of technology to aid in spearheading improved performance within the banking sector. Now, one of the greatest discoveries of our time has been the use of technology in aiding management to make efficient decisions regarding management, service delivery and overall organizational plans. This has resulted in better information management compared to the traditional frameworks of information management.
In the traditional framework, reports could be compiled, and then printed during free time in order to aid in the process of making decisions. Consequently, it proved burdensome on decision makers as they could find it hefty perusing through the all the organizational information in order to come up with a single decision. Unfortunately, such a system became very disturbing especially in the daily running operations because it required reports through complicated procedures characterized by ad-hoc queries that could be received from various senior managers and then submitted to the strategic managers for analysis and decision-making. Moreover, the desire to query ad-hoc information proved impossible sine this could disturb operations especially in the banking, airline, and other sectors. And as a result, banks have embraced this new trend in a very significant improvement in terms of decision making, confidential information management, and better customer service management. Similarly, as a result of the increased usage of decision support systems, banks have been able to improve in their overall operations, as they can now calculate both the costs and returns at an ad-hoc manner and be able to estimate departments that are not making impact in the overall banking sector progress. Therefore, this research paper shall indulge in an evaluation procedure, aimed at discovering the effect of the DSSs on the overall progress of the banking sector.
Objectives of the Study
The general objective of this study is to find out the impact of the Decision Support System in the E-Banking sector. The study will however take into consideration, the overall banking industry.
Specific objectives of the study include;
What is the ratio of customers using e-banking services compared to those using the direct banking services?
What is the general outcome of embracing DSS in e banking?
Significance of the Study
This study is very important owing to the vitality of the banking industry in today’s globalized world. It is very important to note that almost all business organizations depend on the services of the banking sector, ranging from salary disbursement, to bill payments, to mortgage management, to loan advancement, to name but a few. Furthermore, great deals of businesses invest their money in the banking industry and therefore require having their finances managed with the best information system. The bottom line here is that the banking industry has a very intricate link within society. Therefore, by analyzing on the impact of DSSs in the banking sector, the research shall simultaneously provide an important discovery on how efficient decisions from DSSs could help promote social welfare, whilst enabling the banking industry to grow in terms of returns and efficiency in service delivery
The research shall address the following questions;
To find out whether the use of decision support system has been successful in the banking sector
To find out the manner in which DSS has been implemented in the e-banking industry
To find out the manner in which decisions are made in the banking sector through the use of DSSs
The Null Hypothesis:
The use of DSS in the e-banking sector has led to speedy and efficiency in the decision making process
The use of DSS has led to tremendous increase in returns from the e-banking sector
The use of DSS in the e-banking industry has rather reduced the process and quality of decisions made in the banking industry
The use of DSS has rather increased the amount of costs incurred in banks
This chapter shall present an overview literature regarding the conception and development of the decision support systems generally in order to pave way for a finer research and analysis on the role of the decision support system in the e-banking industry. Therefore, the chapter shall expound on the various concepts and then give a literature review of the approach and interest that has been given on decision support system globally. Of great significance in this chapter is the transformation that has been observed in general regarding management information system, and in particular, the decision support system. Furthermore, it shall also be born in mind that decision support systems promote the highest level of management in the organization, who are responsible for the formulation of policies and decisions. Therefore, the section shall over an overview in various sections of the literature in order to pave way for the research that shall be conducted in the general banking sector.
The emergency of DSSs has undergone an evolvement over the years out of two research areas (Keen, 1978). The Carnegie Institute of Technology undertook theoretical studies on the issue of DSS around 1950s to 1960s, while the Massachusetts Institute of Technology, at around 1960s undertook a more technical project on the interactivity of computer systems. The discoveries regarding this system therefore, led to numerous attempts in the area of research and development, and it became a major issue by mid 1970s. consequently in the 1980s, it had gained more ground, hence group decision support systems (GDSS) and executive information systems (EIS), as well as organizational decision support systems (ODSS) were developed thereafter.
Furthermore, Sol (1987 1) identifies the transformation that has taken place in the DSS scope over the years. For instance, in 1970, the term DSS designated “a computer based system to aid in decision making” (Sol, 1987 1), however towards the end of 1970s the trend moved to the consideration of DSS as “interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems” (Sol, 1987 1). Contrariwise, DSSs during the 1980s was inclined towards the use of “suitable and available technology to improve effectiveness of managerial and professional activities” leading to challenges in the quest to develop intelligent workstations (Sol, 1987 2).
An earlier success in the realm of DSS was witnessed during the implementation of the Gate Assignment Display System (GADS) by the Texas Instrument, on the United Airlines (Efraim, Turban, Jay E. Aronson, Ting-Peng Liang, 2008 574). The implementation of this system led to drastic reduction in the travel delays through efficient management of ground operations starting with the O’Hare International Airport, Chicago, and then the Stapleton Airport, Denver Colorado (CBR Staff Writer, 1987). Thereafter, there was increased progress pertaining to this are of DSSs where for, in the 1990, developments regarding data warehousing as well as On Line Analytical Processing (OLAP), took center stage. This has therefore, led to better mechanisms pertaining to the process of decision making, with greater scope in reporting technologies. This has led to the inclusion of DSS in managerial processes of an organization. Most importantly, there has been a lot of concern over the issue of DSS in the educational environments, implying its overwhelming support.
The use of DSS has been received with great interest across the global business and organizational environment. The banking, insurance, health-care, and retail businesses have increasingly used what has been referred to as ‘accumulated operational data’ in order to deeply know and operate their businesses (Cho Insurance Data Mining). This trend has been characterized by the accumulation and reconciliation of data from various operations of a corporation. And this accumulated reservoir of information has been referred to as ‘data warehouse’. It is therefore this data warehouse which serves as source of information for analysts to extract and use in the process of making well-informed decisions. Therefore, analysts do make their decisions using and interactive DSS that is referred to as ‘On Line Analytical Processing (OLAP) (Cho Insurance Data).
The quest for DSSs has been facilitated by the increased information availability, which in return has led to development of new technologies aimed at assisting organizations in establishing efficient data storage and retrieval framework. In this framework huge volumes of data stored in warehouses are accessed efficiently and in an organized manner.
Therefore, analysts use a combination of both data warehouse and OLAP technique in order to make efficient decisions. This method was brought into focus by Codd , Coddy, and Sally ( 1993), who aimed at providing what Dr. Cho and Dr. Ngai refers to as ‘point-and-click simplicity’ in the systems of decision support (Cho, and Ngai, p.5) . Upon its introduction, the implementers of the current systems of executive information embraced it. However, it has lately experienced challenges especially regarding the manner in which these techniques are to be integrating in use-friendly interfaces in order to promote decision support systems, and also on how to integrate it into data mining frameworks (Cho, and Ngai, p.5).
Decision-making executives across the various organizations have been supported first, by well-designed data mining techniques. The most popular data mining technologies include decision tree technology, discriminant analysis technology, and neural network technology.
Discriminant Analysis: this statistical technique makes use of linear functions in order to make distinctions between groups. Well, the efficiency linear discriminant functions depend on the covariance in the distributions; moreover, the distributions have to be Gaussians, as well as equal in terms of covariance (Cho, and Ngai, p.6). Conversely, and as a result of its simplicity, discriminant analysis has still received support from people. However, the training error must be minimized in order to identify a linear discriminant function; the training error is the one that points out the exact incurred losses during the classification of the sets of training examples (Cho, and Ngai, p.6).
Neural Networks: this is a technique in computing which received impetus from the functioning of the nerve cells within the brain. It works as parallel-interconnected units that operate and execute their functions and send the relay their outcomes to neighboring units (Cho, and Ngai, p.7). In a typical order, nodes connect to each other in a manner that each node has a connection to all the other nodes in other layers. Normally, before finally agreeing on the system, a lot is done in the establishment, with a lot of system learning and bias reduction until the system’s algorithms are designed to effectively coordinate as a single unit performing complex roles. This therefore makes neural systems capable of accommodating both linear and non-linear systems. Notwithstanding, neural networks can accommodate all sorts of environment, be it contradictory or confirmatory. Furthermore, as a result of their capacity to learn, they are able to accommodate data which is imprecise, turning it into important information for business and financial matters. However, neural networks are basically limited due to their incapacity to explain their process of attaining certain results.
Decision Trees: decision trees are basically displayed through graphical means, by stems and leaves. Whereby, class identifies the leave, while an object identifies the stem. A decision tree can therefore describe discrete class, which depends on logical expressions regarding their attributes’ value (Cho, and Ngai, p.8). Furthermore, the decision trees are explanatory as well as first in terms of their algorithms. Examples of techniques used to build decision trees include ID3, C4.5, CHAID, or CART, for that matter. However, their finer details will not be significant in this research work.
Data Mining is another area worth of mentioning especially owing to the fact that this research shall all about the banking industry, which deals with huge amounts of confidential data as well as information. Its development came along knowledge discovery growth, and it grew as an interdisciplinary field aimed at ensuring proper management and uncovering of data, which due to time and increased operations within organizations was increasingly huge.
As a result of overwhelming interest on data mining and knowledge discovery, a lot of scholars have written immensely on the subject, with varying perspectives. For instance, Zhou (2003) developed a review on three books on data mining, written from varying perspectives. Regarding databases, the reviewed book was from Han and Kamber (2001), while regarding machine learning, it was Witten and Frank, (2000), and finally the statistical book by Hand et al. (2000). Hence, the book was interest in expounding on the techniques of rule induction (Triantaphyllou and Felici, 2006).
Nevertheless, the use of data mining and knowledge discovery is very significant in areas with rich information, and featuring high knowledge benefits. That is why Berry and Linoff (2004) came up with various areas and types of data mining in areas of sales, marketing, as well as customer support. Most importantly, the utilization of data mining systems in the banking sector have been analyzed by Hormozi and Giles (2004), who explored on the impact of this system in marketing, fraud detection, risk management, and customer retention and acquisition.
Definition of Terms
The concept DSS has no universal definition so far. However, Daniel Power has used the aiding mode as the basis for designing definitions of DSS. He therefore, differentiates between data-driven DSS, communication-driven DSS, document-driven DSS, model-driven DSS, and knowledge-driven DSS. An example of communication-driven DSS is that of Microsoft’s NetMeeting. In A Decision Support System (DSS) is an information system that is computer based, which supports organizations in decision-making process.
However, using the criterion of scope, there is enterprise DSS as well as desktop DSS (Power, 1997, 13). A Data Warehouse is an on-line repository of historical enterprise data that is used to support decision making (Inmon, 1996).
OLAP refers to a system in which analysts navigate and retrieve data from various data warehouses (Kimball, 1996).
This chapter shall deal with the manner in which the research process shall be conducted right from the start to the end. It shall therefore act as a sketch map showing the manner in which various issues shall be approached, and the reasons as to why various issues shall be taken into consideration. It shall therefore offer what Bryman and Bell calls “the commonest view of the nature of the relationship between theory and research” (2007 1). The main goal of the research methodology shall be to find out the impact created by the developed decision support systems on e-banking sector in banks. Therefore, all the instruments that will be used, the reasons for their usage, and the manner in which the analysis of the findings shall be done, will be presented. Nevertheless, the research shall utilize both primary and secondary data. It shall also offer both a qualitative and quantitative approach to the research findings, but more emphasis shall be placed on the quantitative findings.
Like any research work, the role of primary data is very significant and can therefore, not be negated whatsoever. Thus, regarding primary sources, the research shall make immense usage of both interviews and questionnaires. All these instruments shall be used in extracting useful information from banking sector employees, especially the Human Resource managers, and other tactical level managers of the banking sector.
Questionnaires: this shall be structure in both open-ended and closed-ended format. The decision to use open-ended questions shall be as a result of the quest to find more information besides the general questions posed. Open-ended questionnaires are able to give room to respondents to present more information compared to the one they could have given had they been provided with closed-ended questions. Secondly, the use of open-ended questions shall be aimed at human resource managers, and other tactical level managers. This is because, these people are able to access huge information in the banks compared to other employees. Furthermore, by providing them with open-ended questionnaires, they can be able to feel in information that could assist in the final recommendations of the research regarding the usage of DSS in the e-banking sector. Other people that shall be interviewed shall be customers of various banks in order to find out their feelings regarding the adoption of decision support systems in the banking sector. However, their overall contribution towards the research recommendations shall be minimal because, DSS is more understood internally than externally.
The use of secondary data shall be equally important in this research. This is because it is through studying the existing literature regarding decision support system that the researcher is going to understand the overall conception and impact of it in the e-banking sector. Therefore, various books, journals, and websites shall be utilized in order to understand better the use of decision support systems. The researcher shall also use secondary sources of information in order to design the necessary questions based on the previous studies so that such answers shall be evaluated alongside the research findings. Secondary sources shall also be used in examining and understanding the manner in which banks have embraced the usage of decisions support systems. The major areas that could be obtained from the secondary sources shall include materials related to the decision support system implementation process. The research shall as well utilize both the qualitative and quantitative measures in research in studying the secondary sources. Therefore, it shall utilize the SPSS programs in analyzing the statistical findings of the e-banking usage. This is a very important approach owing to the nature of banking in dealing with numbers as well as statistical information. Furthermore, this approach shall be aimed at enabling the learner to utilize effectively the skill acquired in SPSS.
Validity and Reliability
In carrying out this research process, the researcher shall also bear in mind the validity and reliability of the data that could be obtained. Therefore, there shall be appropriate measures put in place to ensure that the sample finding reflect closer association with population sample. Therefore, the research samples shall be selected randomly in order to eliminate biasness on the part of the researcher. Even in selecting the banks to obtain samples from, the researcher shall maintain random system of sampling in order to ensure that the banks selected are not simply selected out of personal preference. Another technique that shall be put in place shall be the increased focus on confidentiality. A number of contacted banking employees have indicated worries as to the use of information that they are inclined to give during their responses. To some, they fear that such information might be used for other negative reasons other than the ones specified. But the researcher shall institute high levels of confidentiality, and the information obtained shall be basically for pure academic purposes. Questionnaires shall also be structured in a manner that they do not obligate anybody to provide personal details within the questionnaire.
The research makes the following assumptions:
The research findings of the sampled banking employees shall represent the overall reflection of the banking sector
The data collection instruments shall be accurate and precise
The respondents shall exhibit high levels of honesty, thus accurate information
The overall opinions and interest in decision support systems shall be similar across the banking sector
Scope and Limitations
The scope of this study shall be on the global banking sector, even though the respondents shall come from within the region. This therefore, shall imply that the information obtained shall be limited to only those interviewed. However, the researcher shall attempt to find resourceful information from oversee banks through the use of technology such as telephone calls and emailed questionnaires. However, this approach shall be limited because one can never know whether the responses are genuine or false. Another limitation is on time. The banking sector is a vast area in terms of research study. Therefore, the research shall only be limited du the limited time available. Another limitation shall be on the quality of the responses given. A great deal of employees has expressed worries over the information that they can give, and the surety of confidentiality. This is likely to affect respondents in deciding the kind of information they could be willing to give. In general, limitations shall be experienced in terms of time, money, and scope of study.
RESULTS AND FINDINGS
The research results and findings shall be collected, classified, and summarized based on research questions. Data analysis method that shall be applied will be the use of SPSS, which is a software program that deals with statistical information and provides various results suitable for decision-making.
The use of DSSs has been received with great zeal in the e-banking industry especially in the decision making process. Therefore, from the literature that has been evaluated, it is clear that the research shall act as an efficient tool of evaluating both the pros and cons of the DSS usage. The research shall therefore over a great platform for the improvement and increased usage of the DSS in promoting development in society.
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