Information systems to support business needs
Table of Contents
Contents Page
INTRODUCTION 2
INFORMATION SYSTEM 3
STRATEGIC LEVEL MANAGEMENT INFORMATION SYSTEM 3
OPERATIONAL MANAGEMENT 3
TACTICAL MANAGEMENT 3
STRATEGIC MANAGEMENT 4
HARDWARE AND SOFTWARE 5
DECISION SUPPORT SYSTEMS (DSS) 5
The Dialog Component 6
The Data Component 6
Data Warehousing 7
Data Mining 9
The Model Component 9
GROUP DECISION SUPPORT SYSTEMS (GDSS) 9
EXECUTIVE INFORMATION SYSTEM (EIS) 9
EXPERT SYSTEMS (ES) 10
NEW STRATEGY 11
EXPERT SYSTEM - LOAN EVALUATION 11
THE NEED FOR MANAGEMENT INFORMATION TO SUPPORT THE ORGANIZATION 12
CONCLUSION 13
REFERENCES 14
Introduction
The banking industry is facing a time of fierce competition, mostly due to the effects of deregulation and the increase in the number of institutions. It is important that the top managements are being present with qualitative information at a timely manner for high-level decision-making to support strategic management.
Information systems can helps to support business needs, reduce costs and assess risks. In this report, a new strategy such as expert system is presented to lower operating costs and increase productivity as well as profits.
Information System
Information system is a critical resource for today's businesses in supporting business goal and organizational systems. It represents the fundamental elements of a business - its people, work, processes, structure and the plan that enables them to work efficiently to achieve business goals.
Strategic level management information system
Figure 1.1 emphasizes the type if information required by decision makers in a bank is directly related to the level of management decision-making.
Operational Management
A Branch Manager would mostly be concerned with current operating performance status information, says at monthly intervals. Branch Manager would like to know the results of his day-to-day operation in terms of revenue earned, interest and costs incurred margins as also overheads for the Branch or Region as a whole. However, he would required a more detailed report and internal data to find out how he has done during the month as compared to his budget and also what is the cumulative position from the beginning of the budget year. The emphasis is to use the information for exercising the necessary operational control required and for attaining pre-determined operational results and therefore, such information has a strong operational control connotation.
Tactical Management
As distinct from this, the executive management level managers are concerned about a somewhat broader time span. Their concerns would most probably relate to issues such as the average cost of funds received, average earnings rate from fund lent, liquidity and etc, which will determine the effectiveness and efficiency of the bank's operations over a period of time, say, one year. It is not that the executive management is not concerned with operating results, but the presentation of such results have to be much more concise and summarized because to them it is current operational performance status information rather than management control information. They are more concerned about trends in operations - particularly cumulative indicators of such trends compared to the norms assumed at the budget-making time, so that they can initiate the necessary tactical and managerial action for correction of any adverse results, e.g. larger deployment of people in selected areas requiring management support, recovery or some of the things which are highlighted in the analysis of management information received by them. It can be seen that the focus at the executive management level in relation to management information relates to the use of such information for purposes of management control.
Strategic Management
However, when we come to the top management, we find that the traditional performance reporting system becomes quite inadequate and often of doubtful value and relevance. If we assume that the role of the top management is substantially different from the roles of those managers who are at the executive management and operating management levels, it is clear that the traditional performance reporting systems hardly serves their purpose. The top management has the following responsibilities:
* To define the long term direction of the organization, consistent with its resources and the opportunities that the "market-place" offers and the demands of external environment - particularly social, political and regulatory environments - makes, such direction has to be spelt out in a manner which has specificity in terms of what the executive management and the operating management should strive to achieve.
* To define the long-term goals, say, at least for the next five years, in board quantitative and qualitative terms, so that they can serve as reference points for operations.
* To solve unstructured problem and deal with uncertainty and to ensure that remedial action plans are implemented.
* To continuously interact with the external systems including the Government, other banks and financial institutions, etc.
The pressure to improve management decision-making, results from both competitive push and technological pull. In the competitive environment, executive needs to make critical decision and this would be support by various information systems like Decision making systems (DSS), Executive information systems (EIS) and Expert systems (ES).
Hardware and software
Decision making is a process that involves a variety of activities, most of which deal with the handling of information and a wide variety of computer-based tools and approaches have evolved to support these decision making process.
Decision Support Systems (DSS)
DSS are computer-based information systems designed to help decision makers solve problems in ill-structured decision-making areas. Successful DSS applications have addressed problems and decisions in a broad range of managerial and policy environments. By definition, ill-structured decision-making environments are those not well enough understood to permit complete analytical description. ...
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Hardware and software
Decision making is a process that involves a variety of activities, most of which deal with the handling of information and a wide variety of computer-based tools and approaches have evolved to support these decision making process.
Decision Support Systems (DSS)
DSS are computer-based information systems designed to help decision makers solve problems in ill-structured decision-making areas. Successful DSS applications have addressed problems and decisions in a broad range of managerial and policy environments. By definition, ill-structured decision-making environments are those not well enough understood to permit complete analytical description. This implies the need to combine managerial experience and judgment with quantitative computer-based approaches.
Thus DSS are primarily symbolized as providing a set of opportunities directed toward improving the effectiveness and productivity of managers, strengthening the organization's competitive and survival ability, and rationalizing the decision making process within an organizational context. They aim at realizing the desire for accurate, timely and relevant information to help individual managers in organizations deal with an increasingly turbulent economic environment and the growing pressures of competition.
DSS components include dialog, data and modeling. This conceptualization is based on dialog between the user and the system, the data that supports the system and the model that provide the analysis capabilities. A good DSS should have balance among the three capabilities. It should be easy to use, to support the interaction with non-technical users; should have access to wide variety of data and should provide analysis and modeling in numerous ways.
Figure 1.2 Components of a DSS
Figure 1.2 shows the relationships between the three components. The software system in the middle of the figure consists of the database management system (DBMS), the model base management system (MBMS) and the dialog generation and management system (DGMS).
The Dialog Component
The attributes of the dialog component can be called a "dialog style." For example, one dialog style requires user to keep a reference card and remember which commands to enter with a keyboard to obtain a printed report. Another dialog style uses a mouse to access pull-down menus and move icons on a color screen to get a graphical presentation of analysis results. The explosive growth of Microsoft's Windows for PC and the Macintosh's interface popularized this dialog style. The current standard is the browser interface.
The Data Component
Data is either accessed directly by the user or is an input to the models for processing. It is critical for DSS to use all the important data sources within and outside the organization. The concept of data sources must be expanded to information sources, moving beyond only accessing database records to accessing documents containing concepts, ideas and opinions that are important to decision making.
Figure 1.3 showed the four kinds of information that result from considering external and internal information that contains either (1) information based on data records such as found in data files or (2) document-based information such as reports, opinions, memos and estimates.
Figure 1.3 Four Types of Information
The data record is the collection of the attributes necessary to describe the entity. Related records form a file, a group of files form a database and a collection of databases can comprise an information system. Finally a "data model" (hierarchical, network, relational and object) describes the relationships among the entity and attributes. An organization as large as the bank is very complex. The data models representing the bank are also complex. To assist in managing these data model, it be structured into a number of model views with a hierarchy structure, which represents subsets of data and information of interest to the bank.
Document-based is generally package in multimedia documents. The attributes that describe the concept are symbols such as words, diagrams, photos, sounds, etc. A group of related concepts could be packaged as a document, a set of related documents is stored in a file cabinet or file server and a set of those make up a library. Models of document-based information might include classification systems, keywords, hypertext link networks, and so forth.
Ideally a data dictionary should be considered as soon as DBMS is considered. An ideal sequence is to (1) set up the data administration function, (2) develop data standards, (3) install a DBMS and (4) install data dictionary as the first database application.
Data Management organizes internals fact into data record format. Information management, on the other hand, focus on concept (such as ideas found in documents, especially digital documents such as web pages from both internal and external sources. Thus "information resources" contain richer universe of digitized media, including voice, video, graphics, animation and photographs. Managing this expanded array of information resources require new technology - Warehousing.
Data Warehousing
A data warehouse enables operation data to be examined and analyzed historically from many different perspectives. This is called "multi-dimensional analysis". For example, a data warehouse enables factors contributing to the profitability of a branch to be analyzed using software packages that carry out Online Analytical Processing (OLAP). Operational data, summarized in data warehouse, can also be analyzed historically using DSS, EIS and ES. This warehouse can be use to analyze bank operational databases so that time-dependent trends can be determined from different perspectives such as region, product, type of customer.
With a data warehouse:
* Reduction of inflexible, hardcopy reports that are not responsive to business needs.
* A saving in ad-hoc programming costs and time: desktop tools enable managers and their staff to obtain information without having to wait for IT resources to perform the work.
* Instant access to data to satisfy immediate information needs of management, it will remove delays caused by multiple interactions between managers and IT staff, with intrinsic possibilities for misunderstanding.
* Timeliness: delays in access to information incur considerable costs for the Bank - due to an inability to respond promptly and wasted resources, both human and material
* Sophisticated end user tools for user-friendly access - with a capability to "drill down" through different levels of data summarization and aggregation in an information warehouse, from the big picture to fine detail. A manager can examine the result of an information request and request further information to be provided immediately.
* Trend analysis: the opportunity to examine trends over time and respond where needed to achieve outcomes.
* "What if" scenarios: the ability to create hypothetical situation and access their affect on the bank. E.g. "if manpower in an area was to change in a defined way, how would this affect costs and product delivery?"
Customer data would be recommended and included in a marketing data mart in the data warehouse. A marketing data mart is a focused subset of data, for analysis by marketing manager and staff within the bank. It can be use to discover how to more effectively market to customers as well as non-customers with the same characteristics. As a result, the marketing department has a large part to play in driving force behind warehouses. They can use customer data from billing and invoicing systems, for example to identify customer clusters and see the effect different marketing programs have on these clusters.
The Benefits of Data Warehousing
* Definition of goals and objective in strategic plans indicates information that will be required by managers. Some of this can be summarized from operational databases on a periodic basis and printed in reports. Other analysis of information can be done in a data warehouse.
* Software packages that help managers analyze information and present results in graphical, tabular or report format on a desktop computer are readily available for data warehouse. These packages are Executive Information Systems (EIS), Decision Support Systems (DSS) and Online Analytical Processing (OLAP) systems. Key criteria for the purchase of EIS, DSS or OLAP packages are ease of use and flexibility for specification by managers of any required analysis and presentation formats.
* Data warehouse delivers information from many perspectives or dimensions; this is called "multi-dimensional analysis". It enables managers to examine change trends over time. Demographic change trends (or other population changes) that affect needs for banking products and services. It help management access the most effective ways of delivering those products and services to satisfy the needs are all of great interest to the management.
* Data warehouse can deliver this information to a manager's desktop in head office or in bank branches throughout the country and overseas. It enables bank managers to interrogate data at the branch level, to analyze data themselves and to create ad-hoc reports without time-consuming and expensive programming to develop inflexible hardcopy reports.
* A data warehouse explicitly associates time with data. Data in a warehouse is valid for a period of time, say, 5-10 years.
The overall objective for a data warehouse is to increase the productivity and effectiveness of decision-making in organizations. This, in turn, is expected to deliver competitive advantage to organizations.
Data Mining
Data Mining uses advanced statistical techniques to explore data warehouse looking for previously unknown relationships in the data, such as which clusters of customers are the most profitable. Similarly the massive amount of document-based information is organized into document repositories and analyzed with document mining techniques.
The Model Component
Models provide the analysis capabilities for a DSS. Using a mathematical representation of the problem, algorithmic processes are employed to generate information to support decision-making. The models in a DSS can be thought of as a model base. A variety of models can be included: strategic, tactical and operational, as well as model-building blocks and subroutines. Each type of model has unique characteristics.
Group Decision Support Systems (GDSS)
GDSS are systems that support decision making by more than one person, working together to reach a decision. Group decision-making may result from a sequential process in which one person makes a decision (or part of a decision) and passes it on to another person, called "sequential interdependent" decision-making. Or several people may reach a decision jointly by working together simultaneously and interacting, which is called a pooled interdependent decision-making. GDSS is enables to allow meeting participants to simultaneously "talk", when the computer sorts and sends ideas to each of the terminal, all at the same time. That saves a tremendous amount of time, because all these are done electronically instead of manually, and the time saved will enable participants to spend more time manipulating and expressing their ideas. This can consequently increase the productivity and efficiency of the group. The time-consuming benefit also has an added bonus: when productivity and efficiency in meetings increase, it is likely that the team spirit can be consolidated, resulting in an increase of the strength of binding among team members. One of major benefits of meeting support systems is improved meeting efficiency and effectiveness. Meetings are more efficiency when the ideas generated by the group are more creative and group member committed to the groups' activities is great. Meetings are effective when group commitment happens more quickly.
Executive Information System (EIS)
EIS can be use by executives for the following purposes.
* Company performance data: Executive can browses the sales, production, earning, budgets and forecast reports to detect possible problem.
* Internal communications: Personal correspondence, reports and meeting materials can put on EIS. Executive can has enough information or preparation time to make the necessary decisions. It can also be use for strategic planning. EIS helps to make this crucial work more efficient and result in better plans, especially across divisions. Instead of each division preparing plans that can be combined, EIS allow executive to explore interrelationship between plans and activities at several divisions.
* Environmental scanning: Through the same source, Executive can investigates whether competitors have introduced a new product or launch an effective ad campaign. They can also get view news on government regulations, financial and economics developments and scientific subjects.
EIS can be view as an aid to dealing with important needs that involve the future health of the organization. It will also increase executive performance and reduce time wasted looking for information.
Expert Systems (ES)
ES (or knowledge-based system) is a computer program that emulates the thought process of a human expert. The knowledge base becomes a form of combined data/model base. The inference engine can be viewed as a knowledge base management system similar to the DBMS and the dialog management system. The language system then is part of the dialog.
ES (Figure 1.3) is built around a rule base that incorporates knowledge, algorithms and heuristic rules. The process of creating rule base begins with a human expert whose expertise is captured, encoded by a knowledge engineer and entered through knowledge acquisition facility. In addition to the rule base, ES also incorporate a database, a model base and a graph base.
A user accesses the system through a user interface called the expert system shell and enters the parameters of a problem to an inference engine. Often, the expert system shell incorporated natural language processing. The inference engine uses the input parameters to access the rule base, database, model base and graph base. Based on the available information and its reasoning capability, the inference engine reaches a conclusion and offer expert advice. ES also contain an explanation facility that reproduces the logic the inference engine followed reach its conclusion.
Figure 1.3 Expert system components
In the banking industry, ES can use to supports tasks in capital resource planning, loan application analysis and strategic planning.
New Strategy
"Nearly 5,000 bankruptcy petitions filed in just 12 months" by The Business Times on November 23, 2003.
As jobs become scarcer, more and more people take a longer time to find jobs. This has led to an increase in the number of debt defaulters. All bank loans necessarily contain an element of risk. In order to minimize this risk, it is essential that procedures and control mechanisms are in place to ensure that each loan is objectively accessed.
Expert System - Loan evaluation
Implementing computing technology for delivery of financial services is one-way bank can reduce cost and possibility of loan default. Artificial intelligence (AI) systems, particularly expert systems, are computer-based technology finding a place in the banking industry. ES are computerized advisory programs that try to imitate or substitute the reasoning processes and knowledge of experts in problem solving. It will analyze the problem, manipulate the encoded human-expert knowledge and provide a recommendation based on inference heuristics and rules of thumb. That is, the system is based on flexible, human-like thought process. Expert systems are of great interest in business and scientific communities because of their potential to enhance productivity and to augment work forces in areas where human experts are becoming harder to find and retain. Such systems can also be expected to improve the quality of the bank's asset base and increase overall profitability. Current applications are restricted to relatively limited and narrow areas of expertise.
The expert system can help officer analyze client credit worthiness by weighing qualitative information against operating performance, making historical results and such. Since the technology applies expertise only in the context of each specific case, it can be more effective and thorough than static guidelines.
The major advantage in automating loan evaluations is the improvement in the bank's lending record through the uniform application of credit and security guidelines. That is, the system will ensure awareness of and adherence to relevant bank policies. Decision-making is more consistent and evaluations are more accurate that may result in more applications being accepted and fewer losses through defaults. ES can help develop experienced, productive loan officers more rapidly by improving their understanding of critical aspects of a business. Productivity increases of 8%-15% appear possible with corresponding improvements in the credit area. The use of expert systems should encourage the thorough and thoughtful analysis of new loan applications without the need for time-consuming supervision by more experienced officers. Lastly, the knowledge base is easily updated and maintained when the bank changes loan policies or the government alters credit regulations.
The loan evaluation expert system is design to incorporate qualitative factors such as a loan officer's intuition, experience and judgment as well as qualitative factors. It also provides an opportunity to put loan evaluation expertise at the disposal of non-experts.
The Need for Management Information to Support the Organization
The traditional performance reporting formats, which report the results of operating performance for the month and cumulatively from the beginning of the budgeting year, is only of limited value. Given the content of strategic and operational responsibilities of their role, the major requirements of information - in addition to the summarized organizational performance report - should be related to information such as trends in external environment, trends in effectiveness of the bank's operations, performance of the competitive banks and plans relating to the future expansion. Executive would require systematic information systems to support strategic management decision-making. Management information is particularly good at extracting the best alternative from a long list of options. Over time, as users interact with systems, the system senses patterns in those interactions, incorporates the new knowledge into its knowledge base and in effect, learns. It can capture and stores expertise in a permanent, consistent, affordable, well documented, easily transferred form. In contrast with artificial expertise, human expertise is perishable, difficult to transfer and document, unpredictable and expensive.
Expert systems represent one of the successful areas of artificial intelligence. These systems are computer programs that mimic the knowledge and decision-making ability of a human expert on a particular subject. They can help inexperienced people. They not only can improve the quality of decision-making but they are a lot less experience than the cost of consulting a human expert before every decision.
Conclusion
It have been observed from what has been presented above that the top management must not only take steps for summarizing operating performance status information but more importantly, be constantly presented with information (and be briefed) about the likely future scenario relating to the external and internal operations. Only with the help of information systems that such information is available, would the top management of commercial banks be able to discern trends in advance, initiate policy and tactical changes, shift their strategies and guide their organization to the attainment of the desired long-term goals and objective.
References
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7. The Business Times - November 23, 2003
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