Papers
Selected From
Centre
for Business Information, Organisation & Process
Management (BIOPoM) 1st International
Conference 2006
University of Westminster ,
London UK
A Review Of Business Intelligence Approaches To Key Business Factors
In Banking
Goran Radonić, Croatian Institute of Technology, Katarina Ćurko,
University of Zagreb
ABSTRACT:
Business Intelligence (BI)
supports decision-making capabilities of banks and helps them to attain and
maintain a competitive advantage in today’s turbulent markets. Its usage
makes the conditions, procedures and mechanisms for creating business
knowledge. BI supports reaction to external pressures and enables effective risk
management and implementation of regulatory compliances such as Basel II
accord. The purpose of this paper is to outline BI techniques and their role in
analysis of key business factors in banking industry
Keywords: Business Intelligence (BI), Data Warehouse (DW),
On-Line Analytical Processing (OLAP), Data Mining (DM), Key Business Factors
(KBF), Banking, Financial Industry
1. Introduction
Financial
institutions’ long history of computing has created a collection of data
that is measured in pentabytes. Large banks produce
hundreds of millions of transactions daily which are stored inside complex IT
systems. Efficient analysis of these data is crucial for a success in the
financial market. Detection and suppression of fraud, risk management, customer
management, banking product management, and loss prevention are some of the
primary concerns of institutions providing financial services. BI technology
can collect and transform millions of records for comprehensive analysis.
Financial institutions exploit BI technology to analyse and understand the
behaviour of their clients, to better satisfy their clients’ needs in an
endless chase for a competitive advantage in the market.
The paper gives a review of
BI techniques and their role in banks’ customer and risk management
applications as well as implementation of regulatory compliances. This paper is
structured as follows. Section 2 outlines the business intelligence and its
main features. Section 3 describes the environment of banking operations. The
role of BI technology in analysing key business factors in banking with a brief
review of typical applications is presented in section 4. At the end, the main
conclusions are drawn.
2. Business Intelligence
Business intelligence is
the ability of an organisation to understand and use information to its gainful
operation (Osterfelt, 2000). Enterprise BI is a way
that brings synergies to business processes and new efficiencies across
business areas (Liautaud, Hammond, 2000). BI offers
to enterprises „one version of truth“, providing consistent and
harmonised data to every department in an organisation (Bochner,
Vaughan 2004). How can one achieve data consistency (also known as “one
version of truth”) across different applications in a complex
organisation? There are three important goals that need to be accomplished in
order to achieve data consistency (Arents, 2003):
Ø
Timeliness: the data within system should be synchronised with all other
applications;
Ø
Accuracy: the data should encompasses every data from any other
application;
Ø
Acceptance: the users, convinced of timeliness
and accuracy of data, should be able to actively use the system as support for
decision making.
In today's companies, BI
plays an important role in support of the decision making process to augment
competitiveness, making an efficient link between business strategies and IT.
Business intelligence technology has been continuously expanded and improved
and more and more complex business questions can be answered using these
technologies. The most widely used business intelligence enabling technologies,
described in more detail below, are: data warehousing (DW), on-line analytical
processing (OLAP), and data mining (DM).
3. Data Warehousing
A data warehouse (DW) is an
integrated collection of historic detailed and summarised data that is supplied
by the spider web environment from internal and external data sources. It is
organized by business areas (subject oriented) and is user-friendly, especially
for a manager and business analyst. The original label that pre-dates the data
warehouse is still the best description of what we are designing: a decision
support system (Kimball, Ross, 2002).
The most delicate part of
the data warehousing is the extraction of data from various data sources with
variable data quality. It has to be decided which internal and external data
will be fed into the warehouse, and how the inconsistencies among data sources
will be resolved. Large amounts of operational data is accessed by end users
and stored in different systems and the same data are represented differently
in different systems (Turban et al, 1999).
Benefits of the data
warehouses are most obvious in companies with several computer platforms and
versions and with many different data sources. The most important benefits
organisations seek from their DW efforts are: better business intelligence
(39%), reduced time to locate, access and analyse information (21%),
consolidation of disparate information sources (20%), strategic advantage over
competitors (11%), faster time-to-market (5%), and replacement of older
decision support systems (3%) (Hall, 1999).
3.1. OLAP
The best known knowledge
discovery techniques are online analytical processing (OLAP) and data mining
(DM) techniques (Turban et al., 1999). OLAP provide users with the means to
explore and analyse large amounts of data, involving complex computations,
their relationships, and visually present results in different perspectives.
OLAP tools are a combination of analytical processing procedures and graphical
user interface. The key features of an OLAP application are: multidimensional
views of data, calculation intensive capabilities and time intelligence (Forsman, 1997).
A multidimensional view of
data that is usually used in OLAP applications provides quick and flexible access
to data and information. Typical applications performed on multidimensional
data views are: roll-up (data is summarized with increasing
generalization), drill-down (increasing
levels of detail are revealed),
slice and dice (performing projection operations on the dimensions), and pivoting (cross
tabulation is performed) (Jarke et al, 2000). Complex analyses are possible, such as
time series (sequence of events) and model charting, forecasting, modelling,
statistical, and “what-if” analysis.
Presentation of information
via user interface in the form of text, picture and graphics determines the way
to execute queries and display of query results. It is important that the
interface enables pleasant work within a graphical environment, with simple and
fast running of queries, and a visually appropriate display of query results.
The OLAP technology
potentially provides several benefits to an organisation: increases the
productivity of business managers, analysts, and whole organisation by the
inherit flexibility and timely access to strategic information; leverages IT
developers to deliver solutions to business users faster; and it provides the
ability to model real business problems and to respond more quickly to market
demands (Forsman, 1997).
3.2. Data Mining
In contrast to OLAP being
retrospective in nature (Turban et al, 1999), data mining provides prospective
knowledge discovery. Data mining is a process of discovering meaningful new
correlations, patterns, and trends by sifting through large amounts of data
stored in repositories, using recognition technologies as well as statistical
and mathematical techniques.
Data mining technology
discovers hidden trends and patterns in large volumes of data. A significant
distinction between data mining and other analytical tools is in the approach
they use in exploring the relationships among the data. The analytical tools
usually support a verification approach, in which the user hypotheses about
data interrelationships are verified or refuted. This approach relies on the
intuition of the analyst to pose the question and his or her ability to refine
the analysis based on the results of potentially complex queries against a
database.
Data mining uses
discovery-based approaches in which pattern matching, clustering, neural
networks, genetic, and other algorithms are used to determine the significant
relationships and correlation among data (Kennedy et al, 1998). Data mining
algorithms can look at numerous multidimensional data relationships
concurrently, highlighting those that are dominant or exceptional. Data mining
enables users to discover knowledge and provides them with greater depth and
understanding of data than ad hoc querying and using of OLAP applications.
4. Banking Environment
4.1. Overview
Banks operate in one of the
most dynamic environments: new markets are being opened, new products are being
launched, new competitors enter markets that were previously reserved only for
banks, new regulatory requirements are being imposed, and new customer needs
are being identified. Rapid external changes and high pressures affect banking
operations with immediate impact on development of banking IT systems.
Continuous innovation and launch of new products with ever shortened life cycle
has led to development of many non- or loosely connected applications making
banks IT a heterogeneous collection of systems and data. With mergers and
acquisitions occurring in the banking sector all over the globe,
heterogeneousness of systems and data is augmented. The need for unified
resource of information for decision-making led to an integrated collection of
data known as the data warehouse. More and more companies worldwide use and/or
develop the data warehouses. Usage of techniques and tools for extracting
useful knowledge from the available data is necessary for a bank that has to
respond to the business pressures.
Banks operate in a complex
environment, as depicted in Figure 1. Rigorous competition and market
requirements dictate bank’s operations which are further restricted and
regulated by several national and international authorities who demand constant
and prompt reporting and auditing to assure supervisors and stock holders of
stability. Currently, all international banks and national banks of most
countries are implementing, or make plans and preparations to implement the
‘Basel II accord’ (BIS 2005). The implementation of Basel II accord
relies heavily on the bank’s IT infrastructure, particularly on BI and DW
systems. To manage newly introduced component of operational risk, requires new
data and databases. Detailed logs of
banking business processes become a new source of data that will be subject to
BI analysis in order to optimize bank’s processes and minimize
operational risk.
Considering the way banks
operate and use their data, the banks’ needs for information can be
divided into two basic categories: Customer Management and Risk Management data
and applications.
4.2. Customer Management
The customer is a focus of
all business activities. This is not specific to banking, almost any company is
struggling to understand who the customer is, what the customer wants, when,
how, and why the customer wants it. It has become essential for companies to
find new ways to attract new customers, to maximize the value of each existing
customer, and to retain the most profitable ones (Liautaud,
Ø
Control in every aspect of relation with clients;
Ø
Means to recognise and retain the most profitable customers;
Ø
New ways to attract new customers (from competition);
Ø
Efficacy of its processes and profitability of products;
Ø
Understanding of new markets and need for new products.
Moreover, the financial
industry is, and will be more, oriented towards the selling of new products
than toward traditional services such as offering loans and holding deposits.
That makes a modern bank’s employee more a salesman than a traditional
banker. Armed with timely and accurate information, a modern banker knows all
about his or her customer, and all the bank’s services that would be
appealing for that particular customer, as well as profitable and
risk-acceptable for the bank.
Having a strategy to
leverage modern information technologies to gain an operational efficiency,
enhance customer service, raise productivity and profitability, the banking
industry is becoming less focused on its core business of holding deposits and
giving loans, and more on managing information (Girish,
2001).
4.3. Risk Management
Due to the nature of its
business, risk management is inherent to financial industry. In banking there
is an ever present risk of payment default, fraud, theft, identity theft, and
operational risk connected with internal procedures and processes.
The implementation of the
new Basel II regulatory compliance (BIS 2005) means interconnection of IT
systems with processes to gain higher transparency and reliability of
bank’s operations. Evaluation and prediction of market changes and
minimisation of capital reserves are also in focus of Basel II. Compliance with
Basel II accord generated a need for central repository of purified, sorted,
and aggregated data about financial transactions and associated risks. A data
Warehouse is a central and crucial component of a software solution for Basel
II enabling a flexible and modular infrastructure for risk management. Relevant
surveys (Furlonger, McKibben,
2005) (Bender, Ding, 2005) made a comparison of modern software risk solutions
and identified the following common Basel II components:
Ø Risk data warehouse;
Ø Risk analytics/risk engine;
Ø Loss severity and probability estimations;
Ø Data management and integration;
Ø Optimization and management of collaterals;
Ø Asset liability management;
Ø Applications for reporting and regulative compliance;
Ø Credit portfolio management.
The same surveys revealed
the following significant components found in some Basel II solutions:
Ø
Global limits management – used for consolidation of various
exposures to risk for real time control;
Ø
General ledger reporting and IAS compliance;
Ø
Risk databases - consolidated third party data about operational risks;
Ø
Profitability management – financial modeling
and analysis of possibilities of capital allocation using business rules,
expected losses and profitability of clients for pricing of products.
5. Bi Technology And Key
Business Factors In Banking
5.1. General Description
Key
business factors (KBFs) are measures or indicators
that are significantly related to the business success of a particular company
or industry. Their uses contribute to the overall improvement of results and as
such have to be closely
and continuously monitored. Defining KBFs requires
specific knowledge and understanding of a bank’s core business and
services, behaviour and habits of its clients and the ways its services are
being used. Implementation of KBF monitoring requires BI tools.
BI can provide business
value by helping enterprises identify risk early and identifying material
changes in business condition that require attention (Friedman, Hostmann, 2004). BI technology can collect and show data
about each client, account, and service, and provide an aggregate panoramic
view of business performance. BI enables efficient analysis of key business
factors such as:
Ø
Assets and liabilities analysis
Ø
Risk analysis
Ø
Revenue analysis
Ø
Client profiling
Ø
Account analysis
Ø
Campaign analysis
Ø
Sales analysis
Ø
Customer loyalty analysis
Ø
Customer care analysis
Ø
Credit scoring
In addition to financial
KBF such as profitability and efficacy which can be derived from financial data
in general ledger, other factors can roughly be categorised as either customer or risk
related.
5.2. Customer Related Key Business Factors
Client information can be
scattered across multiple accounts and bank’s divisions and that
challenges client analysis. BI technology through data warehousing techniques
accumulates these scattered data and with appropriate tools (OLAP and DM)
identifies clients with multiple accounts and their assets in order to develop
an appropriate approach to the individual client.
One of a bank’s
profit drains are delinquent accounts. The term applies to credit product lines
whose clients consume credit and fail to make payments. The occurrence of
delinquent accounts has to be promptly detected and anticipated. BI techniques
enable grouping of accounts by geographical, demographical and psychological
variables. The accounts that don’t fit usual standards can be identified
and monitored.
The 80/20 rule is being
applied particularly in the banking industry where the top 20% of the customers
generate 80% of the revenue. BI techniques enable identification of the best
revenue generating clients across countries, regions, cities and even
affiliates and help to develop business strategies and approaches for different
client segments. Depending on the client’s size, revenue, and needs, new
products and services appealing to the customer and revenue generating for the
bank, are being promoted.
Understanding of how its
products and services are being used is crucial for a bank’s efficiency
in customer relations. Applications for profiling, segmenting, and credit
scoring of clients are founded on BI technologies. Customer segmentation is an
important area of CRM where BI tools can be particularly useful for producing
finer grained segmentation that result in better focused marketing campaigns.
Customer value management is another area where BI tools can be used to predict
which customer segments are likely to become more profitable in the future.
Marketing campaigns are
both revenue and cost generating. To make a profitable campaign it is important
to predict the campaign’s scope and size in terms of cost, media type
being used, duration, etc. BI tools can easily measure the client’s
response to a campaign, perform cost and benefit analysis and measure the
overall results. These results and findings become a part of a bank’s
knowledge base repository for future use and campaign enhancement.
BI applications for sales
help banks analyse new accounts from different perspectives such as product
category, client profile, and geography. The sales perspective gives a better
view on marketing campaign results, helps to understand the results and market
trends, and leads to better results. In selling to existing customers a
probability of a positive customer response is calculated. Thus, an outcome of
a marketing campaign which targets carefully selected customers can be very
appealing and have very high response rate with low costs. By offering the
right products to the right clients, overall customer relationship and customer
loyalty are being improved. Profitability is also increased since the costs of
selling to new potential customers are several times higher than selling to the
existing ones.
The capability to attract
and attain a customer is key to long term
profitability. BI application for loyalty analysis monitors the duration of the
customer relationship, the span of services used, and
measures demographical, geographical and psychological impacts. Customer care
for any service providing organisation is of great importance, especially in
the banking industry, where users interact with banks through different
channels and in different ways to accomplish their daily needs. Levels of
customer satisfaction can be measured by analyzing the contact history with the
client. Data warehouse and data and knowledge mining tools give an integrated
view of clients through their contact history, interactions with the bank and
their contribution to the bank’s revenue (Berry, Linoff,
1997).
Availability of enhanced
data helps banks to shape new opportunities for revenue, strengthen
relationships with existing customers, attract new customers, adapt to growth
and development, leveraging existent technologies and skills. BI is a powerful
tool at banks disposal to understand and satisfy their customers and achieve
competitive advantage.
5.3. Risk Related Key Business
Factors
Risk management is essential
for banking and to the financial industry in general. Traditionally, a
bank’s risk managers were highly skilled and experienced employees who,
besides credit scoring and risk assessment, had an important task of training
of younger personnel. Today’s bank
workforce consists of predominantly young, less experienced personnel, whilst
staff with a high level of expertise is either unavailable or too
expensive. Therefore, information,
knowledge, and information technology become the main resource in support of
banking operations.
For example, credit scoring
applications which are founded on sophisticated algorithms and parameters, many
of which are gained through data mining, have largely replaced the need for
human analysis. Another example of successful implementation of BI techniques
is an early detection of credit card theft. Based on the fact that the volume
of transactions following card theft increases rapidly, an on-line comparison
of previous and expected credit card holder behaviour with the actual transactions, can trigger an early warning and suspicion of
card theft. Such systems can save the bank considerable sums otherwise spent to
compensate for losses.
The Basel II accord
requires the gathering of risk data at all organisational levels. The data has
to be relevant to every aspect of bank’s business ranging from customer
to operational data. The ultimate objective of collecting and warehousing risk
data is to increase a bank’s competitiveness in the market. These data
can be categorised as data about losses and data on the effects of risk
management.
The Basel II accord
provides for the collection of data related to credit, market and operational
losses, and other risks such as liquidity and interest rate change risks.
Besides the data about losses, banks collect KRI's
(Key risk indicators, and other data from statistical analysis, stress testing
and simulations on loss data.
The time dimension is the
most important element in risk analysis. The need for historical data is not
only required by Basel II specifications, but it is also required for time
series analysis of economical cycles.
The “risk
engine” and the risk data warehouse are the central components of a Basel
II solution. The risk data warehouse is filled in an organized and structured
manner with purified data about risks in transactions and operations carried
out. The warehouse is the source for credit, market and operative risk
applications, as well as for financial reporting.
To manage risk
successfully, the modern banker must be armed with “intelligent”
information: timely, relevant, and precise information about the current
customer and the current situation the banker is dealing with.
6. Conclusion
For banks to prosper in
today’s complex business environments, specific information and knowledge
about all operational details is required. The bank’s operations and
processes have to be recorded and appropriately stored. This historical data
has to be accessible for analysis and knowledge extraction.
The solution is to create
data warehouses and extract knowledge from the data using BI technology. BI can
leverage tactical and strategic decision making based on the vast amount of
data that is gathered inside bank’s systems. The KBFs
in banking are categorised as being customer and risk related. This customer
related data and BI is exploited in all possible ways to augment a bank’s
sales. The risk data warehouse and BI techniques are the foundation for risk
management and regulatory compliances such as Basel II accord. Finally, we presented
a review of typical BI techniques and their applications in the banking
industry that points to a conclusion that the exploitation of BI in banking is
on an upward trend and that more and more data warehousing and business
intelligence applications are expected to be implemented in the future at all
levels of banking operations.
7. References
Arents-Gregory M., 2003, One Version of
the Truth, DM Direct, DMReview and SourceMedia, articleID=6359.
Bender, L.,
Ding C., 2006, Risk Management and Basel II: Comparing the Financial and Credit
Risk Solution Vendors, Report Published by Celent
LLC, www.celent.com.
BIS 2005,
Bochner P., J. Vaughan, 2004, BI today: One
version of the truth, Application Development Trends, 2004, VOL 11; NUMB 9,
pages 18-24, ISSN 1073-9564.
Forsman, S., 1997, OLAP Council White Paper, OLAP
Council,
Friedman T., Hostmann B., 2004, The cornerstone of Business Intelligence
Excellence, Gartner Research, Decision Framework, Research Note, DF-21-9470, www.bi.umich.edu/startup/download/bi_cornerstones_gartner.pdf
Furlonger, D., McKibben,
D., 2005, Magic Quadrant for
Girish, G.V. 2001, Banking Business Unit
– Challenges and Achievements, Infosys Technologies Limited,
Annual Investor Meet,
Hall, C., 1999, Data
Warehousing for Business Intelligence,
Jarke, M., Lenzerini, M., Vassiliou, Y., Vassiliadis, P.,
2000, Fundamentals of Data Warehouses, Springer Verlag.
Kennedy, R.L., Lee, Y., Van
Roy B., Reed, C. D., Lippmann, R. P., 1998, Solving Data Mining Problems
through Pattern Recognition, Prentice Hall, Upper Saddle River, NJ.
Kimball, R., Ross, M.,
2002, The Data Warehouse Toolkit – The Complete
Guide to Dimensional Modeling, Wiley.
Liautaud, B.,
Osterfelt, S., 2000, Business Intelligence:
The Intelligent Customer, DM Review www.dmreview.com/editorial/dmreview/print_action.cfm?EdID=3107
Turban, E., McLean, E., Wetherbe, J., 1999, Information Technology for Management: Making Connections for Strategic Advantage, John Wiley & Sons.
Contact the Authors:
Dr. Goran
Radonic, Croatian Institute of Technology, Planinska 1, 10000 Zagreb, Croatia; Phone
+385-1-5494734;Email:.goran.radonic@hiteh.hr
Dr. Katarina
Curko, Faculty of Economics & Business University
of