Description
The main objective of this paper is to elaborate how Business Intelligence (BI) as a knowledge management tool could help consultants in providing professional services to the financial sector.
American Journal of Information Systems, 2014, Vol. 2, No. 2, 26-32
Available online athttp://pubs.sciepub.com/ajis/2/2/1
©Science and Education Publishing
DOI:10.12691/ajis-2-2-1
Business Intelligence as a Knowledge Management Tool
in Providing Financial Consultancy Services
Gul Muhammad, Jamaludin Ibrahim, Zeeshan Bhatti
*
, Ahmad Waqas
Kulliyyah of Information and Communication Technology, International Islamic University Malaysia
*Corresponding author: [email protected]
Received November 21, 2013; Revised February 18, 2014; Accepted March 07, 2014
Abstract The main objective of this paper is to elaborate how Business Intelligence (BI) as a knowledge
management tool could help consultants in providing professional services to the financial sector. The Business
Intelligence (BI) solution could be a competitive advantage for the consultants if they are able to exploit the
Business Intelligence (BI) tools and technology such as Data Warehouse, Data Mining, On-Line Analytical
Processing (OLAP) and Extraction Transformation Load (ETL). The consultants can use Business Intelligence (BI)
solution to analyze the organizational data such as structures and business processes of the Financial Institution. By
analyzing the organizational data, the financial institution can imp better rove and streamline functional efficiencies
to not only bolster up sales and marketing strategies and better develop customer services program, but also mitigate
risk by developing more appropriate risk management actions. In brief, by having this competitive advantage, the
consultant will be able to withstand in the market, which is always changing.
Keywords: Business Intelligence (BI), Knowledge Management (KM), Data Mining, Data Warehouse, Extraction
Transformation Load (ETL), On-Line Analytical Processing (OLAP)
Cite This Article: Gul Muhammad, J amaludin Ibrahim, Zeeshan Bhatti, and Ahmad Waqas, “Business
Intelligence as a Knowledge Management Tool in Providing Financial Consultancy Services.” American Journal
of Information Systems, vol. 2, no. 2 (2014): 26-32. doi: 10.12691/ajis-2-2-1.
1. Introduction
In 2008, Business Intelligence (BI) was the number one
technology priority for the third year in a row (Gartner,
2011). It has become the top presidency of Chief
Information Officer (CIOs) since it (BI) can have a direct
positive impact on business performance of an enterprise.
The capability to complete the task by making brighter
decision at every level of the business is another
significance that what Business Intelligence (BI) can
improve dramatically.
Most leading corporations expect personnel in every
role to seek fresh and intelligent ways to improve
performance, increase employees’ effectiveness, and grow
profit and stronger customer relationship. In order to
achieve these expectations, Business Intelligence (BI) is
the answer. Supporting decision-making at every level,
modifying managers, executives and knowledge actors to
take the most efficient action in given situations are the
reasons why most leading organizations require Business
Intelligence (BI) as a essential element.
Business Intelligence (BI) strategy must be aligned with
the organization objectives, advance business and
improves knowledge management. Business Intelligence
strategy (BI) helps organization in creating the best utilize
of information with tactical, strategic and operational
decision-making.
Generally, the use of Business Intelligence (BI) in
financial services has provided values. A survey
conducted by Gartner, Inc found that more than 95 percent
of banking answerers agreed that Business Intelligence
(BI) is a strategically first step driven by senior
management. The respondents are from banks, insurers
and nonfinancial businesses. Gartner, Inc also found that
more than 90 percent agreed they received the value
awaited from their Business Intelligence investment.
Nowadays, most financial institution depends on
Business Intelligence (BI). Financial services include
banking (saving and loans, commercial banks, mortgage
banks, credit union), securities and exchange (brokerages,
investment banks, investment advisor), and international
finance. The financial institution could exploit Business
Intelligence (BI) as a competitive advantage.
In this competitive market age, financial sectors must
have strategies to survive. Generally, a financial sector has
huge amount of data that they process everyday, which is
stored in their complex system. The efficient analysis of
the data is very important and will determine the success
of the financial industry. The way a Financial Institution
in analysing the fraud, risk and customer behavior are very
critical. Business Intelligence (BI) has been used for a
years in order to help a company to solve this kind of
problems, because Business Intelligence (BI) can handle
huge amount of data for the comprehensive analysis.
2. Literature Review
27 American Journal of Information Systems
2.1. Business Intelligence (BI)
(BI) comprises wide variety of applications for
analysing, gathering, storing and making data easily
accessible to help users to make better business processes.
A good Business Intelligence (BI) definition must
encompass both business purpose and technical
functionality. Business Intelligence (BI) tools that are
widely used are Data Warehouse, Data Mining, Extraction
Transformation Load (ETL) and On-Line Analytical
Processing (OLAP) (See Figure 1).
Figure 1. Business Intelligence
2.2.Data Warehouse
Data warehouse is an integrated collection of the
summarized and historic data, which is collected by the
spider web environment from internal and external data
sources. Data warehouse is user friendly especially for
business analyst and manager (Radonic, 2007). It collects
relevant data to the repository where it is validated and
organized to serve the decision-making objectives (Rao &
Kumar, 2011).
2.3. Data Mining
Data mining is a process of discovering patterns,
correlation and trends by modifying through the large
amount of data, which stored in the warehouse.
Recognition technologies, statistical and mathematical
techniques are normally used in Data Mining technology.
2.4. Extraction Transformation Load (ETL)
Extract, transform and load - is set of actions by which
data is extracted from numerous databases, applications
and systems, transformed as capture, and loaded into
target database - including, but not limited to, data
warehouses, data marts, analytical applications, etc.
2.5. On-Line Analytical Processing
(OLAP) technology allows users to explore and analyse
a huge amount data, involving complex computation and
their relationship. On-Line Analytical Processing (OLAP)
tools are a combination of graphical user interface (GUI)
and processing procedures, which is produce a visual
result in different perspectives to the users.
3. Knowledge Management
Knowledge Management (KM) is the collection of
processes that govern the creation, dissemination, and
utilization of knowledge. (KM) this is, as the word entails
the power to handle “knowledge” and right knowledge
available to the right people. It is about making sure that
an organization can learn, and that it will be able to
retrieve and use its knowledge assets in current
applications as they are needed. In Peter Drucker paper, he
defines Knowledge Management (KM) as the
coordination and exploitation of organizational knowledge
resources, in order to create benefit and competitive
advantage.
Knowledge Management (KM) is not always about
technology, but also about understanding how the people
work, brainstorming, identify groups of people who work
together and how they can share and learn from each other
and in the end the organization learning about their
workers experience and about the leadership the
organization (Arora, 2011).
Figure 2. Business Intelligence Architecture
Figure 3. Knowledge Management
In Rao and Kumar paper, they explain that Knowledge
Management (KM) is the practice to add actionable value
to the information from the tacit to the explicit knowledge
using storing, filtering retrieving and disseminating
explicit knowledge and also by testing and creating new
knowledge. "Knowledge Management (KM) will deliver
outstanding collaboration and partnership working. It will
ensure the region maximizes the value of its information
and knowledge assets and it will help its citizens to use
their creativity and skills better, leading to improved
effectiveness and greater innovation", West Midlands
Regional Observatory, UK.
4. Financial Services
American Journal of Information Systems 28
Financial sector has definition as a class of ancestries
carrying business firm, which offer financial services to
the customer and commercial (investopedia.com). The
financial sector includes insurance companies, real estate,
investment funds and banks. Moreover, the financial
services perform best in low interest rate environments
while in large portion, this sector needs generates the
revenue from mortgage and loans. Furthermore, when the
business is, this sector benefits from the additional
investments.
The challenges in the financial services are:
1. developing and changeable regulative surroundings,
2. Continued focus on risk management,
3. Expansion of products and services,
4. disbursement direction and restructuring,
5. Security and privacy risk keep coming,
6. Ensuring data integrity and proper data management,
7. Model risk management,
8. Derivatives reform,
9. Balancing incentive compensation,
10. In closing.
Wall Street, Fleet Street and Main Street: Corporate
integrity at a Crossroads, A recent survey of the financial
services industry in United Kingdom and United States
revealed the wrongdoing in the financial industry. Among
the key finding are (Wehinger, 2013):
1. 26% of respondents indicated that they had observed
or had first-hand knowledge of wrongdoing in the
workplace.
2. 16% of respondent would commit a crime (inside
trading) if they could get away with it.
3. 24% of respondents believed that financial service
professional need to engage in illegal activity in
order to be successful.
5. Implementation of BI as KM Tools
To implement Business Intelligence as Knowledge
Management tool to provide financial consultancy, many
BI tools are available. Following are some of the example.
1. Enterprise BI tools,
2. Databases or packaged BI tools,
3. Visual Data Discovery tools,
4. Pure reporting tools.
Many organizations, both private and public, are
currently evaluating or deploying Open Source BI tools
(OS BI) like J asperSoft, Pentaho or SpagoBI. These three
leading open source Business Intelligence suites offer a
full range of Business Intelligence capabilities, ranging
from ETL to ad-hoc analysis and reporting.
6. The Limitation of Business Intelligence
(BI) and Knowledge Management (KM)
(Kascelan, 2011) try to elaborate some limitations in
Business Intelligence systems especially on small
companies, the reason are:
1. The initial price of the system is costly which even
can reach one million euros of the big companies.
2. Data mining tools use sophisticated tools and they
require the company to give additional training or
even hire external consultant which increase the costs
of implementation.
3. The time of implementation take a long time (6
months- several years). It gives disadvantages to the
company which have limited financial assets.
4. Uncertainty in the success of implementation. The
research from Gartner reveals that 2.000 data
warehouse projects, only 20% are succeed.
5. A poor quality of source data is responsible for the
majority of the time and cost overruns during the
implementations. This is because the small
companies has obsolesce of standard information
system.
Based on (J oo & Lee, 2006), they doing research on the
factors which are lead to dissatisfaction to the knowledge
management’s users, they divided the factors by two
categories:
1. Restriction factors of System Quality:
i. Time and Space: time and space limitation in the
Knowledge Manamgent (KM) system use and
limitation of access methods.
ii. Inconvenience: the degree of discommode of the
system which is resulting the slow response and
imbalance.
iii. Knowledge search: the limitation of keyword-based
search and also limited knowledge categorization.
iv. Knowledge consolidation: the restrictions in
integrating of heterogeneous systems as knowledge
resources and integration of the existing Knowledge
Management system with the Web resources.
2. Limitations factors of Knowledge Quality:
i. Incongruence and rawness of Knowledge: the degre
of incongruence or incompleteness of knowledge
proffered by the KM system.
ii. Untrustworthiness of Knowledge: the degree of
inaccuracy and untrustiness of knowledge proposed
by the KM system.
The result of the research are have significant
affirmative answers, the limitations factors for system
quality such as search and integration, inconvenience and
system quality positively affect user dissatisfaction with
the Knowledge Management (KM) system. Moreover, the
limitations components of knowledge prime such as
incongruence/ inexperience and untrustiness and increase
the dissatisfaction.
Figure 4. Business Intelligence (BI) and Knowledge Management (KM)
29 American Journal of Information Systems
Figure 5. Business Intelligence (BI) and Knowledge Management (KM)
in Decision Making
Business Intelligence (BI) makes for as educing
worthful information and discover hidden patterns in
internal and outside source of data. The main aim is to
meliorate knowledge on the information which accords
the top manager to create efficient decision to achieve
organizational objectives. But, the majority of
organizational knowledge is in unstructured form or in the
minds of the employess. Furthermore, Knowledge
Management (KM) plays to encompasses both tacit and
explicit knowledge within the organizations increase the
organization carrying into action by providing cooperative
tools to create, acquire and contribute the knowledge
within organization. (Khan & Quadri, 2012).
Business Intelligence (BI) and Knowledge Management
(KM) are the main tools to achieve the organizational tool
by providing the environment which users receive, desired
and reliable and also timely information or knowledge.
The organizations need both BI and Knowledge
Management (KM) as an integrated system to get value
from explicit and implicit knowledge (Khan & Quadri,
2012).
7. The Similarities and Differences
between Business Intelligence and
Knowledge Management
Fundamentally, Business Intelligence and Knowledge
Management have the same aspires. Attaining the level
best degree of empathizing of one’s controlling
environment and pertinent considerations that can be bring
forward or delay advancement toward an aim is one of the
purposes of Knowledge Management (KM). No matter
how the same principle implements to the idea of Business
Intelligence (BI). Supporting strategic decision-making,
growing the business and monitoring competitor of
organization are the purpose of conducting Business
Intelligence (BI). Undoubtedly, there are absolute
similarities between Knowledge Management as well as
Business Intelligence.
Knowledge Management and Business Intelligence are
established on information technology. They depend on
the Internet, hardware, software, database storage
technology. Besides that, their application in business
processes both includes accumulating, collating, dealing
and the use of information and knowledge. In addition,
both accomplish their function depending on information
and knowledge. It undoubtedly trues that Knowledge
Management (KM) and Business Intelligence (BI) is
interacting and complements each other.
Generally, the focus of Knowledge Management (KM)
is cognition. It specifically interested about people have
good noesis, cultural behavior. It also stressed the
significance of the knowledge innovation and whether it is
leveraged effectively. In the same way, Business
Intelligence (BI) initially concentrated on technology and
data, the applied effect of which in fact is closely related
to the skills of user as people normally use quantitative
analysis of technical expertise to solve business problem
with the assistance of business intelligence system.
There are some fundamental differences, while both
Business Intelligence (BI) and Knowledge Management
(KM) concepts have similarity high-level objectives. The
differences are to be found in the manner in which they
are applied toward achieving that goal. While the value of
Business Intelligence (BI) and its product, opportunity
analysis is found in its usefulness as a decision making
tool, the value of Knowledge Management (KM) relies in
the ability of the organization to identify, capture and
reuse knowledge and in particular best practices in such a
manner that can save the organization time, effort and
resources.
Another difference between Knowledge Management
(KM) and Business Intelligence (BI) is the intension.
Business Intelligence (BI) developed gradually through
transactional serving systems, like administrative
information system, management information systems and
decision support system. Knowledge Management (KM)
is the management idea and methods in the development
of the knowledge economy era. It emphasized that
knowledge is most important resource and strategic capital,
the corporate competitor advantage relies on knowledge
creation, dissemination and utilization.
Beside the connotation, both have difference in the
focus. Business Intelligence mostly deals with data
resources. As its aim to make information resources
orderly and structured, the whole process of Business
Intelligence (BI) is relatively closed and independent.
Business Intelligence (BI) also focuses to the combining
and integration of the external morphology of information.
Opposite side, the Knowledge Management (KM) system
deals with knowledge resources, knowledge sharing and
innovation are the primary goals of it. For organization,
while Business Intelligence (BI) manages with objective
information in the real world, Knowledge Management
system tends to action immanent and personal knowledge.
Lastly, the difference between Business Intelligence (BI)
and Knowledge Management (KM) is the core technology.
The core technology of Knowledge Management also
imply in document management, groupware engineering
science, text mining, retrieval technology, enterprise
knowledge portals and so on, Business Intelligence (BI)
on the other side attach more than meaning to data
analysis and its core technologies consist of data
warehousing, online analytical processing, data mining
and enterprise portals.
8. Business Intelligence (BI) and
Knowledge Management in Financial
Sector
American Journal of Information Systems 30
Business intelligence has gained acceptance in the most
financial sector, even though there are many definitions of
Business Intelligence (BI), however Knowledge
Management (KM) has mixed response. Knowledge
Management concept in organization has struggled
because they frequently attempted to enforce huge
enterprise-wide knowledge management projects failed.
The complexity is another reason why the implementation
failed.
Figure 6. Banking Operation
Figure 6 shows how the strict and rigorous competition
and market in bank’s operation which are further regulated
and restricted by several international and national
authorities who demand prompt and constant auditing and
reporting to assure the stockholders and supervisors of
stability (Radonic, 2007). The figure also shows customer
relationship management and business intelligence is
needed in relationship to the clients and Human resource,
Business Intelligence and business process management to
their employee.
The globalizations, mergers, deregulations and
acquisitions, competition from non-financial institutions
and technological innovations make the company to
always to rethink about their business strategy. In this case,
financial services have to create new revenue streams,
enter new markets, gain market share and reduce
operational cost and also must concern about the customer
expectations.
The application of Business Intelligence (BI) in banks
can be summarized as follows: (Rao & Kumar, 2011)
Bank performance: It is about analysing the historical
data of the institution to make decision on the future. The
key performance indicators are deposit, credit, profit,
income, branches, employees etc.
Marketing: Marketing is the most widely used for data
mining in the banking industry. Usually it is been used for
analyse the customers, in term of their preferred product
or services.
Risk management: With the uncertain and changing
market, it’s also affected financial situation. Lack of the
knowledge may have great risk in the future customer.
Especially in banks there are present risk of payment
default, fraud, theft and operational risk connected with
internal procedures and processes.
Customer segmentation: Most business activities are
focusing on the customer. All business activities must
understand who their customer is. The segmentation is a
method of grouping customer based on their character or
patterns which will help the organization to understand
whom and where the product target.
Fraud detection: According to Decker, there two
approaches to detect the fraud. First, the bank taps the data
warehouse of a third party and use the data mining to
identify. Second, using bank’s own internal information
Budget planning: Try to understand the performance
indicators from a specific area and calculated from the
existing information from the system.
Figure 7. Knowledge Management (KM)
The Figure 7 shows the knowledge management
technologies. In a traditional Business Intelligence (BI),
the system must provide capabilities such as business
process management, collaborative portal, and business
planning software, portals and content management
systems and be able to support more timely data feeds.
Figure 8. Knowledge Management (KM) with Business Intelligence (BI)
The Figure 8 illustrates the knowledge cycle on how the
Knowledge Management (KM) can help the business to
improve their processes. The figure shows how the
Business Intelligence which contents information becomes
the central role in knowledge management. It has data,
business context, decision, action and the collaboration of
experts.
31 American Journal of Information Systems
In order to support all major financial and accounting
activities, most financial institutions have implemented
software. Customer Relationship Management and
Supplier Management software are the most powerful
systems that have become trend in managing relationship
with customers and suppliers. However, the main purpose
of these systems is to record, organize and retrieve
information that resides in their specific database. But,
these systems are not built as analysis tools.
Although some systems offer limited analytical features,
but the features are cover only their own application’s
database and cannot be used with other systems in the
company. Consequently, it is very difficult to get access to
summarize as well as detailed information through a
single user interface. So, the people who responsible for
operating different systems will have a great deal in
writing, maintaining and printing their report for
management. This is the reason for one of today’s great
frustration of corporate managers and analysts.
There are some limitations and issues in decision-
making based on static reports from different systems:
1. Information overload,
2. Lack of information,
3. No interactivity,
4. Lack of unified cross-database analysis tools.
Implementing a Data Warehouse with modern Business
Intelligence (BI) software can be the solution to the
problem of a poor analytical environment in a company.
The poor analytical environment will have multiple data
sources, different report writers and lack of analytical
tools.
It is finally becoming feasible for financial institution to
implement Data Warehouse that can be updated and
maintained with relative case by utilizing modern ETL
(Extraction Transformation Load).
A common data repository is resulted, which provides
decision makers with endless possibilities for
investigating (Data Mining) and analysing variances,
trends and exceptions. Because a modern Business
Intelligence (BI) solution feeds off a frequently updated
Data Warehouse that includes detailed information. This
becomes a tool for any person within the organization or
related to it who needs easy and fast access to summarized
and detailed information from across the database of
company and it becomes much more than just a tool for
executives.
There are some key features that offered by many
Business Intelligence tools. The some key features are:
1. Drill-down,
2. Graphs, charting and trees,
3. Exception highlighting,
4. Pivot rows and columns,
5. Drag and drop dimension into the current view or to
the background,
6. Custom calculations,
7. Queries,
8. Comments,
9. Combo views,
10. Dashboards,
11. Business Intelligence (BI) and web portals,
12. Distribution of cubes/reports,
13. Other popular analytical features (ranking, filtering,
sorting, etc).
Most financial manager found difficulties in asking new
data from one or more of the corporate information
systems to the Information System (IS) department.
Because, the feedback is rarely what financial manager
asked for. Not all people in Information System (IS)
department would understand about finance smatters such
as the difference between debit and credit, Year To Date
(YTD) or periodic balance.
In some leading organizations, they are specializing
some of their Information System (IS) staff by hiring
Information System (IS) personnel into the accounting
department trying to deal with these barriers. This is an
expensive solution and does not necessarily solve the
problem.
In order to meet the never-ending request for more
financial information, the proactive approach for the
financial department seems to be to become self-supplied
with timely financial information and better quality on the
data.
Over last few years, there are many companies have
succeeded to create proactive approach by creating Data
Warehouse that host data from disparate sources and
making them available to end user through Excel, Internet
browser and On-Line Analytical Processing (OLAP) or
managed query tools.
9. Conclusion
In conclusion, this paper describes how business
intelligence plays its role as knowledge management tool
to give benefit to the financial sector which always has
fast-changing market and vast-amount of data. The
business intelligence plays role to extracting the hidden
patterns and valuable information from internal and
external source of data. Moreover, the knowledge
management has roles to cover the tacit and explicit
knowledge within the organizations, which has function to
enhance its performance. It can be said that business
intelligence sustains the knowledge management to
maintain and enhance the performance of financial
organization.
The leverage of Business Intelligence (BI) as a
Knowledge Management (KM) tool could be competitive
advantages for the financial consultancy. Because,
Business Intelligence (BI) solution helps consultants
provide professional services to the financial sector.
Business Intelligence (BI) solution might be the
collaboration between any Business Intelligence (BI) tools
and concepts. So, the consultant must have a competitive
advantage to remain in the global market that keeps
changing every time.
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doc_799753146.pdf
The main objective of this paper is to elaborate how Business Intelligence (BI) as a knowledge management tool could help consultants in providing professional services to the financial sector.
American Journal of Information Systems, 2014, Vol. 2, No. 2, 26-32
Available online athttp://pubs.sciepub.com/ajis/2/2/1
©Science and Education Publishing
DOI:10.12691/ajis-2-2-1
Business Intelligence as a Knowledge Management Tool
in Providing Financial Consultancy Services
Gul Muhammad, Jamaludin Ibrahim, Zeeshan Bhatti
*
, Ahmad Waqas
Kulliyyah of Information and Communication Technology, International Islamic University Malaysia
*Corresponding author: [email protected]
Received November 21, 2013; Revised February 18, 2014; Accepted March 07, 2014
Abstract The main objective of this paper is to elaborate how Business Intelligence (BI) as a knowledge
management tool could help consultants in providing professional services to the financial sector. The Business
Intelligence (BI) solution could be a competitive advantage for the consultants if they are able to exploit the
Business Intelligence (BI) tools and technology such as Data Warehouse, Data Mining, On-Line Analytical
Processing (OLAP) and Extraction Transformation Load (ETL). The consultants can use Business Intelligence (BI)
solution to analyze the organizational data such as structures and business processes of the Financial Institution. By
analyzing the organizational data, the financial institution can imp better rove and streamline functional efficiencies
to not only bolster up sales and marketing strategies and better develop customer services program, but also mitigate
risk by developing more appropriate risk management actions. In brief, by having this competitive advantage, the
consultant will be able to withstand in the market, which is always changing.
Keywords: Business Intelligence (BI), Knowledge Management (KM), Data Mining, Data Warehouse, Extraction
Transformation Load (ETL), On-Line Analytical Processing (OLAP)
Cite This Article: Gul Muhammad, J amaludin Ibrahim, Zeeshan Bhatti, and Ahmad Waqas, “Business
Intelligence as a Knowledge Management Tool in Providing Financial Consultancy Services.” American Journal
of Information Systems, vol. 2, no. 2 (2014): 26-32. doi: 10.12691/ajis-2-2-1.
1. Introduction
In 2008, Business Intelligence (BI) was the number one
technology priority for the third year in a row (Gartner,
2011). It has become the top presidency of Chief
Information Officer (CIOs) since it (BI) can have a direct
positive impact on business performance of an enterprise.
The capability to complete the task by making brighter
decision at every level of the business is another
significance that what Business Intelligence (BI) can
improve dramatically.
Most leading corporations expect personnel in every
role to seek fresh and intelligent ways to improve
performance, increase employees’ effectiveness, and grow
profit and stronger customer relationship. In order to
achieve these expectations, Business Intelligence (BI) is
the answer. Supporting decision-making at every level,
modifying managers, executives and knowledge actors to
take the most efficient action in given situations are the
reasons why most leading organizations require Business
Intelligence (BI) as a essential element.
Business Intelligence (BI) strategy must be aligned with
the organization objectives, advance business and
improves knowledge management. Business Intelligence
strategy (BI) helps organization in creating the best utilize
of information with tactical, strategic and operational
decision-making.
Generally, the use of Business Intelligence (BI) in
financial services has provided values. A survey
conducted by Gartner, Inc found that more than 95 percent
of banking answerers agreed that Business Intelligence
(BI) is a strategically first step driven by senior
management. The respondents are from banks, insurers
and nonfinancial businesses. Gartner, Inc also found that
more than 90 percent agreed they received the value
awaited from their Business Intelligence investment.
Nowadays, most financial institution depends on
Business Intelligence (BI). Financial services include
banking (saving and loans, commercial banks, mortgage
banks, credit union), securities and exchange (brokerages,
investment banks, investment advisor), and international
finance. The financial institution could exploit Business
Intelligence (BI) as a competitive advantage.
In this competitive market age, financial sectors must
have strategies to survive. Generally, a financial sector has
huge amount of data that they process everyday, which is
stored in their complex system. The efficient analysis of
the data is very important and will determine the success
of the financial industry. The way a Financial Institution
in analysing the fraud, risk and customer behavior are very
critical. Business Intelligence (BI) has been used for a
years in order to help a company to solve this kind of
problems, because Business Intelligence (BI) can handle
huge amount of data for the comprehensive analysis.
2. Literature Review
27 American Journal of Information Systems
2.1. Business Intelligence (BI)
(BI) comprises wide variety of applications for
analysing, gathering, storing and making data easily
accessible to help users to make better business processes.
A good Business Intelligence (BI) definition must
encompass both business purpose and technical
functionality. Business Intelligence (BI) tools that are
widely used are Data Warehouse, Data Mining, Extraction
Transformation Load (ETL) and On-Line Analytical
Processing (OLAP) (See Figure 1).
Figure 1. Business Intelligence
2.2.Data Warehouse
Data warehouse is an integrated collection of the
summarized and historic data, which is collected by the
spider web environment from internal and external data
sources. Data warehouse is user friendly especially for
business analyst and manager (Radonic, 2007). It collects
relevant data to the repository where it is validated and
organized to serve the decision-making objectives (Rao &
Kumar, 2011).
2.3. Data Mining
Data mining is a process of discovering patterns,
correlation and trends by modifying through the large
amount of data, which stored in the warehouse.
Recognition technologies, statistical and mathematical
techniques are normally used in Data Mining technology.
2.4. Extraction Transformation Load (ETL)
Extract, transform and load - is set of actions by which
data is extracted from numerous databases, applications
and systems, transformed as capture, and loaded into
target database - including, but not limited to, data
warehouses, data marts, analytical applications, etc.
2.5. On-Line Analytical Processing
(OLAP) technology allows users to explore and analyse
a huge amount data, involving complex computation and
their relationship. On-Line Analytical Processing (OLAP)
tools are a combination of graphical user interface (GUI)
and processing procedures, which is produce a visual
result in different perspectives to the users.
3. Knowledge Management
Knowledge Management (KM) is the collection of
processes that govern the creation, dissemination, and
utilization of knowledge. (KM) this is, as the word entails
the power to handle “knowledge” and right knowledge
available to the right people. It is about making sure that
an organization can learn, and that it will be able to
retrieve and use its knowledge assets in current
applications as they are needed. In Peter Drucker paper, he
defines Knowledge Management (KM) as the
coordination and exploitation of organizational knowledge
resources, in order to create benefit and competitive
advantage.
Knowledge Management (KM) is not always about
technology, but also about understanding how the people
work, brainstorming, identify groups of people who work
together and how they can share and learn from each other
and in the end the organization learning about their
workers experience and about the leadership the
organization (Arora, 2011).
Figure 2. Business Intelligence Architecture
Figure 3. Knowledge Management
In Rao and Kumar paper, they explain that Knowledge
Management (KM) is the practice to add actionable value
to the information from the tacit to the explicit knowledge
using storing, filtering retrieving and disseminating
explicit knowledge and also by testing and creating new
knowledge. "Knowledge Management (KM) will deliver
outstanding collaboration and partnership working. It will
ensure the region maximizes the value of its information
and knowledge assets and it will help its citizens to use
their creativity and skills better, leading to improved
effectiveness and greater innovation", West Midlands
Regional Observatory, UK.
4. Financial Services
American Journal of Information Systems 28
Financial sector has definition as a class of ancestries
carrying business firm, which offer financial services to
the customer and commercial (investopedia.com). The
financial sector includes insurance companies, real estate,
investment funds and banks. Moreover, the financial
services perform best in low interest rate environments
while in large portion, this sector needs generates the
revenue from mortgage and loans. Furthermore, when the
business is, this sector benefits from the additional
investments.
The challenges in the financial services are:
1. developing and changeable regulative surroundings,
2. Continued focus on risk management,
3. Expansion of products and services,
4. disbursement direction and restructuring,
5. Security and privacy risk keep coming,
6. Ensuring data integrity and proper data management,
7. Model risk management,
8. Derivatives reform,
9. Balancing incentive compensation,
10. In closing.
Wall Street, Fleet Street and Main Street: Corporate
integrity at a Crossroads, A recent survey of the financial
services industry in United Kingdom and United States
revealed the wrongdoing in the financial industry. Among
the key finding are (Wehinger, 2013):
1. 26% of respondents indicated that they had observed
or had first-hand knowledge of wrongdoing in the
workplace.
2. 16% of respondent would commit a crime (inside
trading) if they could get away with it.
3. 24% of respondents believed that financial service
professional need to engage in illegal activity in
order to be successful.
5. Implementation of BI as KM Tools
To implement Business Intelligence as Knowledge
Management tool to provide financial consultancy, many
BI tools are available. Following are some of the example.
1. Enterprise BI tools,
2. Databases or packaged BI tools,
3. Visual Data Discovery tools,
4. Pure reporting tools.
Many organizations, both private and public, are
currently evaluating or deploying Open Source BI tools
(OS BI) like J asperSoft, Pentaho or SpagoBI. These three
leading open source Business Intelligence suites offer a
full range of Business Intelligence capabilities, ranging
from ETL to ad-hoc analysis and reporting.
6. The Limitation of Business Intelligence
(BI) and Knowledge Management (KM)
(Kascelan, 2011) try to elaborate some limitations in
Business Intelligence systems especially on small
companies, the reason are:
1. The initial price of the system is costly which even
can reach one million euros of the big companies.
2. Data mining tools use sophisticated tools and they
require the company to give additional training or
even hire external consultant which increase the costs
of implementation.
3. The time of implementation take a long time (6
months- several years). It gives disadvantages to the
company which have limited financial assets.
4. Uncertainty in the success of implementation. The
research from Gartner reveals that 2.000 data
warehouse projects, only 20% are succeed.
5. A poor quality of source data is responsible for the
majority of the time and cost overruns during the
implementations. This is because the small
companies has obsolesce of standard information
system.
Based on (J oo & Lee, 2006), they doing research on the
factors which are lead to dissatisfaction to the knowledge
management’s users, they divided the factors by two
categories:
1. Restriction factors of System Quality:
i. Time and Space: time and space limitation in the
Knowledge Manamgent (KM) system use and
limitation of access methods.
ii. Inconvenience: the degree of discommode of the
system which is resulting the slow response and
imbalance.
iii. Knowledge search: the limitation of keyword-based
search and also limited knowledge categorization.
iv. Knowledge consolidation: the restrictions in
integrating of heterogeneous systems as knowledge
resources and integration of the existing Knowledge
Management system with the Web resources.
2. Limitations factors of Knowledge Quality:
i. Incongruence and rawness of Knowledge: the degre
of incongruence or incompleteness of knowledge
proffered by the KM system.
ii. Untrustworthiness of Knowledge: the degree of
inaccuracy and untrustiness of knowledge proposed
by the KM system.
The result of the research are have significant
affirmative answers, the limitations factors for system
quality such as search and integration, inconvenience and
system quality positively affect user dissatisfaction with
the Knowledge Management (KM) system. Moreover, the
limitations components of knowledge prime such as
incongruence/ inexperience and untrustiness and increase
the dissatisfaction.
Figure 4. Business Intelligence (BI) and Knowledge Management (KM)
29 American Journal of Information Systems
Figure 5. Business Intelligence (BI) and Knowledge Management (KM)
in Decision Making
Business Intelligence (BI) makes for as educing
worthful information and discover hidden patterns in
internal and outside source of data. The main aim is to
meliorate knowledge on the information which accords
the top manager to create efficient decision to achieve
organizational objectives. But, the majority of
organizational knowledge is in unstructured form or in the
minds of the employess. Furthermore, Knowledge
Management (KM) plays to encompasses both tacit and
explicit knowledge within the organizations increase the
organization carrying into action by providing cooperative
tools to create, acquire and contribute the knowledge
within organization. (Khan & Quadri, 2012).
Business Intelligence (BI) and Knowledge Management
(KM) are the main tools to achieve the organizational tool
by providing the environment which users receive, desired
and reliable and also timely information or knowledge.
The organizations need both BI and Knowledge
Management (KM) as an integrated system to get value
from explicit and implicit knowledge (Khan & Quadri,
2012).
7. The Similarities and Differences
between Business Intelligence and
Knowledge Management
Fundamentally, Business Intelligence and Knowledge
Management have the same aspires. Attaining the level
best degree of empathizing of one’s controlling
environment and pertinent considerations that can be bring
forward or delay advancement toward an aim is one of the
purposes of Knowledge Management (KM). No matter
how the same principle implements to the idea of Business
Intelligence (BI). Supporting strategic decision-making,
growing the business and monitoring competitor of
organization are the purpose of conducting Business
Intelligence (BI). Undoubtedly, there are absolute
similarities between Knowledge Management as well as
Business Intelligence.
Knowledge Management and Business Intelligence are
established on information technology. They depend on
the Internet, hardware, software, database storage
technology. Besides that, their application in business
processes both includes accumulating, collating, dealing
and the use of information and knowledge. In addition,
both accomplish their function depending on information
and knowledge. It undoubtedly trues that Knowledge
Management (KM) and Business Intelligence (BI) is
interacting and complements each other.
Generally, the focus of Knowledge Management (KM)
is cognition. It specifically interested about people have
good noesis, cultural behavior. It also stressed the
significance of the knowledge innovation and whether it is
leveraged effectively. In the same way, Business
Intelligence (BI) initially concentrated on technology and
data, the applied effect of which in fact is closely related
to the skills of user as people normally use quantitative
analysis of technical expertise to solve business problem
with the assistance of business intelligence system.
There are some fundamental differences, while both
Business Intelligence (BI) and Knowledge Management
(KM) concepts have similarity high-level objectives. The
differences are to be found in the manner in which they
are applied toward achieving that goal. While the value of
Business Intelligence (BI) and its product, opportunity
analysis is found in its usefulness as a decision making
tool, the value of Knowledge Management (KM) relies in
the ability of the organization to identify, capture and
reuse knowledge and in particular best practices in such a
manner that can save the organization time, effort and
resources.
Another difference between Knowledge Management
(KM) and Business Intelligence (BI) is the intension.
Business Intelligence (BI) developed gradually through
transactional serving systems, like administrative
information system, management information systems and
decision support system. Knowledge Management (KM)
is the management idea and methods in the development
of the knowledge economy era. It emphasized that
knowledge is most important resource and strategic capital,
the corporate competitor advantage relies on knowledge
creation, dissemination and utilization.
Beside the connotation, both have difference in the
focus. Business Intelligence mostly deals with data
resources. As its aim to make information resources
orderly and structured, the whole process of Business
Intelligence (BI) is relatively closed and independent.
Business Intelligence (BI) also focuses to the combining
and integration of the external morphology of information.
Opposite side, the Knowledge Management (KM) system
deals with knowledge resources, knowledge sharing and
innovation are the primary goals of it. For organization,
while Business Intelligence (BI) manages with objective
information in the real world, Knowledge Management
system tends to action immanent and personal knowledge.
Lastly, the difference between Business Intelligence (BI)
and Knowledge Management (KM) is the core technology.
The core technology of Knowledge Management also
imply in document management, groupware engineering
science, text mining, retrieval technology, enterprise
knowledge portals and so on, Business Intelligence (BI)
on the other side attach more than meaning to data
analysis and its core technologies consist of data
warehousing, online analytical processing, data mining
and enterprise portals.
8. Business Intelligence (BI) and
Knowledge Management in Financial
Sector
American Journal of Information Systems 30
Business intelligence has gained acceptance in the most
financial sector, even though there are many definitions of
Business Intelligence (BI), however Knowledge
Management (KM) has mixed response. Knowledge
Management concept in organization has struggled
because they frequently attempted to enforce huge
enterprise-wide knowledge management projects failed.
The complexity is another reason why the implementation
failed.
Figure 6. Banking Operation
Figure 6 shows how the strict and rigorous competition
and market in bank’s operation which are further regulated
and restricted by several international and national
authorities who demand prompt and constant auditing and
reporting to assure the stockholders and supervisors of
stability (Radonic, 2007). The figure also shows customer
relationship management and business intelligence is
needed in relationship to the clients and Human resource,
Business Intelligence and business process management to
their employee.
The globalizations, mergers, deregulations and
acquisitions, competition from non-financial institutions
and technological innovations make the company to
always to rethink about their business strategy. In this case,
financial services have to create new revenue streams,
enter new markets, gain market share and reduce
operational cost and also must concern about the customer
expectations.
The application of Business Intelligence (BI) in banks
can be summarized as follows: (Rao & Kumar, 2011)
Bank performance: It is about analysing the historical
data of the institution to make decision on the future. The
key performance indicators are deposit, credit, profit,
income, branches, employees etc.
Marketing: Marketing is the most widely used for data
mining in the banking industry. Usually it is been used for
analyse the customers, in term of their preferred product
or services.
Risk management: With the uncertain and changing
market, it’s also affected financial situation. Lack of the
knowledge may have great risk in the future customer.
Especially in banks there are present risk of payment
default, fraud, theft and operational risk connected with
internal procedures and processes.
Customer segmentation: Most business activities are
focusing on the customer. All business activities must
understand who their customer is. The segmentation is a
method of grouping customer based on their character or
patterns which will help the organization to understand
whom and where the product target.
Fraud detection: According to Decker, there two
approaches to detect the fraud. First, the bank taps the data
warehouse of a third party and use the data mining to
identify. Second, using bank’s own internal information
Budget planning: Try to understand the performance
indicators from a specific area and calculated from the
existing information from the system.
Figure 7. Knowledge Management (KM)
The Figure 7 shows the knowledge management
technologies. In a traditional Business Intelligence (BI),
the system must provide capabilities such as business
process management, collaborative portal, and business
planning software, portals and content management
systems and be able to support more timely data feeds.
Figure 8. Knowledge Management (KM) with Business Intelligence (BI)
The Figure 8 illustrates the knowledge cycle on how the
Knowledge Management (KM) can help the business to
improve their processes. The figure shows how the
Business Intelligence which contents information becomes
the central role in knowledge management. It has data,
business context, decision, action and the collaboration of
experts.
31 American Journal of Information Systems
In order to support all major financial and accounting
activities, most financial institutions have implemented
software. Customer Relationship Management and
Supplier Management software are the most powerful
systems that have become trend in managing relationship
with customers and suppliers. However, the main purpose
of these systems is to record, organize and retrieve
information that resides in their specific database. But,
these systems are not built as analysis tools.
Although some systems offer limited analytical features,
but the features are cover only their own application’s
database and cannot be used with other systems in the
company. Consequently, it is very difficult to get access to
summarize as well as detailed information through a
single user interface. So, the people who responsible for
operating different systems will have a great deal in
writing, maintaining and printing their report for
management. This is the reason for one of today’s great
frustration of corporate managers and analysts.
There are some limitations and issues in decision-
making based on static reports from different systems:
1. Information overload,
2. Lack of information,
3. No interactivity,
4. Lack of unified cross-database analysis tools.
Implementing a Data Warehouse with modern Business
Intelligence (BI) software can be the solution to the
problem of a poor analytical environment in a company.
The poor analytical environment will have multiple data
sources, different report writers and lack of analytical
tools.
It is finally becoming feasible for financial institution to
implement Data Warehouse that can be updated and
maintained with relative case by utilizing modern ETL
(Extraction Transformation Load).
A common data repository is resulted, which provides
decision makers with endless possibilities for
investigating (Data Mining) and analysing variances,
trends and exceptions. Because a modern Business
Intelligence (BI) solution feeds off a frequently updated
Data Warehouse that includes detailed information. This
becomes a tool for any person within the organization or
related to it who needs easy and fast access to summarized
and detailed information from across the database of
company and it becomes much more than just a tool for
executives.
There are some key features that offered by many
Business Intelligence tools. The some key features are:
1. Drill-down,
2. Graphs, charting and trees,
3. Exception highlighting,
4. Pivot rows and columns,
5. Drag and drop dimension into the current view or to
the background,
6. Custom calculations,
7. Queries,
8. Comments,
9. Combo views,
10. Dashboards,
11. Business Intelligence (BI) and web portals,
12. Distribution of cubes/reports,
13. Other popular analytical features (ranking, filtering,
sorting, etc).
Most financial manager found difficulties in asking new
data from one or more of the corporate information
systems to the Information System (IS) department.
Because, the feedback is rarely what financial manager
asked for. Not all people in Information System (IS)
department would understand about finance smatters such
as the difference between debit and credit, Year To Date
(YTD) or periodic balance.
In some leading organizations, they are specializing
some of their Information System (IS) staff by hiring
Information System (IS) personnel into the accounting
department trying to deal with these barriers. This is an
expensive solution and does not necessarily solve the
problem.
In order to meet the never-ending request for more
financial information, the proactive approach for the
financial department seems to be to become self-supplied
with timely financial information and better quality on the
data.
Over last few years, there are many companies have
succeeded to create proactive approach by creating Data
Warehouse that host data from disparate sources and
making them available to end user through Excel, Internet
browser and On-Line Analytical Processing (OLAP) or
managed query tools.
9. Conclusion
In conclusion, this paper describes how business
intelligence plays its role as knowledge management tool
to give benefit to the financial sector which always has
fast-changing market and vast-amount of data. The
business intelligence plays role to extracting the hidden
patterns and valuable information from internal and
external source of data. Moreover, the knowledge
management has roles to cover the tacit and explicit
knowledge within the organizations, which has function to
enhance its performance. It can be said that business
intelligence sustains the knowledge management to
maintain and enhance the performance of financial
organization.
The leverage of Business Intelligence (BI) as a
Knowledge Management (KM) tool could be competitive
advantages for the financial consultancy. Because,
Business Intelligence (BI) solution helps consultants
provide professional services to the financial sector.
Business Intelligence (BI) solution might be the
collaboration between any Business Intelligence (BI) tools
and concepts. So, the consultant must have a competitive
advantage to remain in the global market that keeps
changing every time.
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