Download 2. Business Intelligence in Banking

BUSINESS INTELLIGENCE IN BANKING
Business intelligence (BI) is a computer based technique used in spotting, digging-out,
and analyzing business data, such as sales revenue by products and/or departments, or
by associated costs and incomes.
"Business Intelligence is a set of methodologies, processes, architectures, and
technologies that transform raw data into meaningful and useful information used to
enable more effective strategic, tactical, and operational insights and decision-making.
Business intelligence also includes technologies such as data integration, data quality,
data warehousing, master data management, text and content analytics, and many
others.
BI technologies provide historical, current, and futuristic views of business operations.
The common functions of business intelligence technologies are reporting, online
analytical processing, analytics, data mining, business performance management,
benchmarking, text mining, and predictive analytics.
Business intelligence aims to support better business decision-making. Thus a BI system
can be called a decision support system (DSS). BI uses technologies, processes, and
applications to analyze mostly internal, structured data and business processes while
competitive intelligence gathers, analyzes and disseminates information with a topical
focus on company competitors.
BI applications in an enterprise:
Business Intelligence can be applied to the following business purposes, in order to drive
business value
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Measurement – program that creates a hierarchy of Performance metrics
and Benchmarking that informs top management about progress towards
business goals
Analytics – program that builds quantitative processes for a business to
arrive at optimal decisions and to perform Business Knowledge Discovery.
Frequently involves: data mining, statistical analysis, Predictive analytics,
Predictive modeling, Business process modeling
Reporting/Enterprise Reporting – program that builds infrastructure for
Strategic Reporting to serve the Strategic management of a business, NOT
Operational Reporting. Frequently involves: Data visualization, Executive
information system, OLAP
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Collaboration/Collaboration platform – program that gets different areas
(both inside and outside the business) to work together through Data sharing
and Electronic Data Interchange.
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Knowledge Management – program to make the company data driven
through strategies and practices to identify, create, represent, distribute, and
enable adoption of insights and experiences that are true business
knowledge. Knowledge Management leads to Learning Management and
Regulatory compliance/Compliance
BI in banking:
BI in banking evolved through Manual Systems to management Information systems
with Computerization. Banks had efficient transaction recording systems before
computerization also. The manual systems too had effectively provided the necessary
reports for management and regulatory requirements. These reports were manually
consolidated at lower offices and final reports were presented at head office level.
These manual systems worked well till the scale of operations of the banks were small.
As the banks grew in size and expanded geographically the number of branch network
grew leaps and bounds and so the, the volume of transactions became quite large and
manual operations became time consuming and error prone. To cater the load of
operations from all bank branches spread across geographies the banks have started
using computers and slowly banks have become fully automated.
The manual management information system (MIS) in the banks had the following
drawbacks:
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The data is laying in different silos
There was a Time lag in data collating.
Data quality is poor.
Unavailability of customer specific data
Data granularity required for developing analytics (what if scenario, drill down)
Was not available to decision makers.
Reporting activity competed with business activity for resources at the branch.
Data classification rules were not applied uniformly across the organization, and
also varied with time.
Slowly, majority of the banks began using information technology for MIS. The
inflexibility of Cobol programmes and batch processing was soon overcome by powerful
desktop systems with rudimentary database systems, which allowed banks to analyse
data, once it has been received in manual form from branches, the same was
transcribed into machine readable formats and validated. Quite a few of regulatory
reports were also produced in this way. These earlier initiatives laid the foundations of
BI in banking.
Uses of BI in banking:
Business Intelligence tools can be used by banks for historical analysis, performance
budgeting, business performance analytics, employee performance measurement,
executive dashboards, marketing and sales automation, product innovation, customer
profitability, regulatory compliance and risk management.
Examples of these applications are;
Historical Analysis (time-series)
Banks can analyze their historical performance over time to be able to plan for the
future. The key performance indicators include deposits, credit, profit, income,
expenses; number of accounts, branches, employees etc. Absolute figures and growth
rates (both in absolute and percentage terms) are required for this analysis. In addition
to time dimension, which requires a granularity of years, half year, quarter, month and
week; other critical dimensions are those of control structure (zones, regions, branches),
geography (countries, states, districts, towns), area (rural, semi-urban, urban, metro),
and products (time, savings, current, loan, overdrafts, cash credit). Income could be
broken down in interest, treasury, and other income; while various break-ups for
expenses are also possible. Other possible dimensions are customer types or segments.
Derived indicators such as profitability, business per employee, product profitability etc
are also evaluated over time. The existence of a number of business critical dimensions
over which the same transaction data could be analyzed, makes this a fit case for multidimensional databases (hyper cube or ‘the cube’).
Analyzing, interpreting and acting upon on the information is a subjective exercise.
Hence, the BI vendor shifted their focus to customer relationship management (CRM).
CRM continues to be the centre of the attraction to banks today and risk management
comes to second.
Customer Relationship Management (CRM):
CRM is at the centre stage of BI in banking. However, it is becoming difficult to assess
whether it is driven by technology or business. Traditional or conservative banking
business models of Indian banking industry relied heavily on personal relationships that
the bankers of yesteryears had with their customers. If we look into the application of
CRM in banking, more closely, CRM is an industry term for the set of methodologies and
tools that help an enterprise manage customer relationships in an organized way. It
includes all business processes in sales, marketing, and service that touch the customer.
With CRM software tools, a bank can build a database about its customers that
describes relationships with sufficient detail so that management, salespeople, service
people, and even the customers can access information, match customers needs with
product plans and offerings, remind customers of service requirements, check payment
histories, and so on.
A CRM helps a bank with the following:
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Find customers
Get to know them
Communicate with them
Ensure they get what they want (not what the bank offers)
Retain them regardless of profitability
Make them profitable through cross-sell and up-sell
Covert them into influencers
Strive continuously to increase their lifetime value for the bank.
The most crucial and daunting task before banks is to create an enterprise wide
repository with ‘clean’ data of the existing customers. It is well established that the cost
of acquiring a new customer is far greater than in retaining an existing one. Shifting the
focus of the information from accounts tied to a branch, to unique customer identities
requires a massive onetime effort. The task involves creating a unique customer
identification number and removing the duplicates across products and branches.
Technology can help here but only in a limited way.
The transition from a product-oriented business model to a customer-oriented one is
not an easy task for the banking industry. This is true in case of all the banks of all the
banks, Indian or otherwise.
For example, even today, in a tech savvy new generation private sector bank there is no
360 degree view of a customer details. They treat the same way a for a credit card
applications to its existing customers as well the new ones.
A retail loan application does not take into account the existing relationship of the
customer with the bank, his credit history in respect of earlier loans or deposit account
relationship. And the private banks are the pioneers in setting up a data warehouse, and
a world class CRM solution.
Most CRM solutions in Indian banks are, in reality, sales automation solutions. New
customer acquisition takes priority over retention. That leads to the hypothesis that it is
BI vendors that are driving CRM models in banks rather than banks themselves. Product
silos have moved from manual ledgers to digital records. An implementation model of
‘relationship’ in Indian banking industry is hard to see as of today.
Most of the BI applications cater to the needs of the top management in banks. But, line
managers have a different set of BI requirements, which differ from those of the top
management. The line managers of banks require operational business intelligence.
Operational Business Intelligence:
Operational BI embeds analytical processes with the operational business structure to
support near real-time decision making and collaboration. This characteristic
fundamentally changes the way how data is used, where it exists and how it is accessed.
Thus ‘Operational BI merges analytical and operational processes into a unified whole’.
This change is rapidly exposing the limitations of traditional analytical tools. Operational
BI helps businesses make more informed decisions and take effective action in their
daily business operations. It can be valuable in many areas of the business, including
reducing fraud, decreasing loan processing times, and optimizing pricing.
Characteristics of Operational Business Intelligence:
Caters to middle management and frontline:
Operational BI delivers information and insights to those managers that are involved in
operational or transactional processes. For example while serving a customer over the
phone if a customer executive get a flash on his computer screen on the likely
requirements of the customer based on his profile and past transaction behavior. This is
an example of operational business intelligence.
Just-in-time delivery:
To manage time sensitive process the needed information should be delivered in near
real-time i.e. within minutes or hours. Operational BI will help in reducing user reaction
for a business issue. The reduced user reaction time with the help of operational BI can
bring business benefits to the organization.
For instance, the ability to detect and react more quickly to the fraudulent use of a
credit card is a good example of how operational BI can provide business value.
By analysing the history of fraudulent situations, the BI system can be used to develop
business rules that signify potential fraud, and operational BI can be used to apply those
rules during daily business operations. The closer to real time the fraud can be detected;
the less is the operational risk.
However, not all operational BI systems need to be near real-time. Reducing action
times to close to zero are is beneficial only in specific types of business requirements
such as the fraud example. In fact, operational BI can be classified into being demanddriven and event-driven, the latter being more automated. If the action time
requirement is a few hours, business users or applications can use the BI system at ondemand analysis and evaluate the results manually to determine whether any action is
required. In the demand-driven case, it is the user who drives the BI system.
But if the action time requirement is two seconds, then on-demand will not be suitable.
In this scenario BI systems must track business operations continuously
and automatically run analyses to determine whether any action is required. If it is, the
business user must be alerted about the situation and sent recommendations on
potential courses of action. In case of a fraudulent credit card transaction, the BI system
is expected to refuse authorisation. In event-driven BI, business operations and the BI
system drive the user. It is obvious that the implementation of event driven operational
BI is more complex than demand-driven BI.
Uses recent transactional data
Data used for operational analysis is frequently accessed before getting loaded into the
data warehouse. The latency in a traditional data warehouse implementation results
from the batch mode in which it is populated. It is more suited for strategic applications
such as historical analysis, risk management, performance management etc. But a
dashboard needs to be as close to transaction data as technically feasible.
Less aggregation, more granularity
In a sharp contrast to traditional BI in which pre-aggregation, with optional drill down to
detail levels is a norm, operational BI normally requires more of data granularity to
address the needs of the specific operational function it supports. Traditional BI aims at
a holistic view of corporate performance, while operational BI is process and user
specific. Yet, some operational BI requirements do require aggregated data, such as the
lifetime value of a customer, which is required for a directed sales call.
Embedded into business processes
Operational BI is intricately connected to transactional business processes. The extent of
this integration depends on the level of implementation. One could use it to generate
operational reports to analyse processes, or monitor them using dashboards and
scorecards. In these two levels there is not much of integration.
In the other two levels, where operation BI is embedded into business processes either
to facilitate them (demand-driven) or to execute other processes (event-driven), it is
embedded into the process.
Handles disparate sources and unstructured data
Traditional databases and data warehouses do not take into consideration the
increasing use of unstructured data; such as emails, telephone calls, letters, internal
notes etc, stored outside these systems, which are of critical value in an operational BI
implementation. Another issue that it has to handle arises out of the disparate
transaction systems in use in most of the banks. The variety of banking services makes it
very complex and often impractical for a single software solution to handle all kinds of
transactions. Extracting data from such disparate systems and making use of
unstructured data is required to be handled by an operational BI system.
Availability is a concern
The high level of integration with transactional business processes demand the same
level of availability from operational BI implementations that transaction processing
systems have to provide. An outage of an operational BI application could have a direct
impact on the organization’s ability to do business or to service its customers.
Therefore, availability becomes a critical issue for operational BI applications.
Requires different architecture
Traditional BI vendors had built their products using proprietary architectures. While
these architectures are ideal for strategic BI, they are not suited for operational BI.
Because operational BI entails coupling BI applications with operation applications and
operational processes, a component-based, service-oriented architecture (SOA) is
necessary to fully support operational BI. Service-oriented architecture that lets users
access real-time knowledge with a set of service feeds can maximize business agility
while reducing complexity. For example, SOA flexibly and cost-effectively supports the
midstream, on-the-fly data collection and analysis necessary for operational BI. Service
orientation also supports operational BI throughout the business by pushing BI data out
to the mobile workforce and enabling workers across the enterprise to incorporate this
vital data into their workflow. The straight-through processing requirements in the
banking industry necessitate immediate risk analysis, which in turn requires an online BI
capability.
Source:
http://en.wikipedia.org/wiki/Business_intelligence
http://www.maiaintelligence.com/pdf/IBA%20Research%20Report%20on%20Operational%20BI.pdf