Busienss intelligence in banking sector

Business intelligence in banking sector

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Naveen Gupta 09BM8033

Vipin Dutt 09BM8091

Business Intelligence in Banking sector

Data warehousing: A data warehouse is a subject-oriented, integrated, time-variant and non-volatile
collection of data in support of management's decision making process.

Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example,
"sales" can be a particular subject.

Integrated: A data warehouse integrates data from multiple data sources. For example, source A and
source B may have different ways of identifying a product, but in a data warehouse, there will be
only a single way of identifying a product.

Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3
months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a
transactions system, where often only the most recent data is kept. For example, a transaction
system may hold the most recent address of a customer, where a data warehouse can hold all
addresses associated with a customer.

Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data
warehouse should never be altered.

A data warehouse is a copy of transaction data specifically structured for query and analysis.

Benefits of Data warehousing:

 A data warehouse provides a common data model for all data of interest regardless of the data's
source. This makes it easier to report and analyze information than it would be if multiple data
models were used to retrieve information such as sales invoices, order receipts, general ledger
charges, etc.
 Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This
greatly simplifies reporting and analysis.
 Information in the data warehouse is under the control of data warehouse users so that, even if
the source system data are purged over time, the information in the warehouse can be stored
safely for extended periods of time.
 Because they are separate from operational systems, data warehouses provide retrieval of data
without slowing down operational systems.
 Data warehouses can work in conjunction with and, hence, enhance the value of operational
business applications, notably customer relationship management (CRM) systems.
 Data warehouses facilitate decision support system applications such as trend reports (e.g., the
items with the most sales in a particular area within the last two years), exception reports, and
reports that show actual performance versus goals.
 Data warehouses can record historical information for data source tables that are not set up to
save an update history.

Applications of Data warehousing: some of the applications data warehousing can be used for are:


 Decision support
 Trend analysis
 Financial forecasting
 Churn Prediction for Telecom subscribers, Credit Card users etc.
 Insurance fraud analysis
 Call record analysis
 Logistics and Inventory management
 Agriculture

Business Intelligence: is a term commonly associated with data warehousing. In fact, many of the
tool vendors position their products as business intelligence software rather than data warehousing
software. There are other occasions where the two terms are used interchangeably. So, exactly what
is business intelligence?

Business intelligence usually refers to the information that is available for the enterprise to make
decisions on. A data warehousing (or data mart) system is the backend, or the infrastructural,
component for achieving business intelligence. Business intelligence also includes the insight gained
from doing data mining analysis, as well as unstructured data (thus the need for content
management systems). For our purposes here, we will discuss business intelligence in the context of
using a data warehouse infrastructure.

A basic understanding of BI (source: Journal of Theoretical and Applied Information Technology)

Benefits of Business Intelligence: BI provides many benefits to companies utilizing it. It can
eliminate a lot of the guesswork within an organization, enhance communication among
departments while coordinating activities, and enable companies to respond quickly to changes in
financial conditions, customer preferences, and supply chain operations. BI improves theoverall


performance of the company using it. Information is often regarded as the second most important
resource a company has (a company's most valuable assets are its people). So when a company can
make decisions based on timely and accurate information, the company can improve its
performance. BI also expedites decision-making, as acting quickly and correctly on information
before competing businesses do can often result in competitively superior performance. It can also
improve customer experience, allowing for the timely and appropriate response to customer
problems and priorities. The firms have recognized the importance of business intelligence for the
masses has arrived.

Some of them are listed below.

• With BI superior tools, now employees can also easily convert their business knowledge via the
analytical intelligence to solve many business issues, like increase response rates fromdirect mail,
telephone, e-mail, and Internet delivered marketing campaigns.

• With BI, firms can identify their most profitable customers and the underlying reasons for those
customers’ loyalty, as well as identify future customers with comparable if not greater potential.

• Analyze click-stream data to improve ecommerce strategies.

• Quickly detect warranty-reported problems to minimize the impact of product design deficiencies.

• Discover money-laundering criminal activities.

• Analyze potential growth customer profitability and reduce risk exposure through more accurate
financial credit scoring of their customers.

• Determine what combinations of products and service lines customers are likely to purchase and
when.

• Analyze clinical trials for experimental drugs.

• Set more profitable rates for insurance premiums.

• Reduce equipment downtime by applying predictive maintenance.

• Determine with attrition and churn analysis why customers leave for competitors and/or become
the customers.

• Detect and deter fraudulent behavior, such as from usage spikes when credit or phone cards are
stolen.

• Identify promising new molecular drug compounds.

Business intelligence and Data warehousing in banking industry:

Banking industry is facing challenges of very tough market requiring highly secure transaction
environment, dicey economics, strict governing regulations and always-demanding customers with
greater levels of expectations. Banks need to develop strategies not only to retain existing
customers, but also attract new customers. This demands identifying and supporting profitable
customers, improved operations at grass root levels and handy action-oriented intelligence on


portfolio performance. For most banks, managing customers effectively at the branch level is critical
to success. Drawing demand deposits from consumers and small businesses funds the crucial lending
activities that drive bank profits. Consider the importance of attracting the right customers,
providing them with the right products, and retaining them in profitable relationships. The branch-
customer relationship, when taken collectively, touches every part of the retail bank. Business
Intelligence & Analytics practice has a strong focus on the banking sector and straddles the spectrum
of analytical solutions in Retail Banking, Consumer Lending, Wholesale Banking, Cards and Payments
and Risk Management. Jumpstart analytical packs around Credit card analytics, Mortgage analytics
and Risk domains covering credit, market and operational risk help customers in accelerating their
implementation timeframe. We have in-depth expertise in executing projects in regulatory
compliance areas on Basel II and IFRS (International Financial Reporting Standards)

Need for Business Intelligence in Banking Sector: following are the few factors define need for the
BI in banking industry

Risk Management

 Probability of loan default and expected recovery of loan default – Important for loan pricing

 Credit cards early detection and prevention of frauds

 Analyzing credit portfolios, enabling banks to quickly identify potential delinquency cases

 Determine overall financial health

 Information about volatility in current economic environment

 Accurately estimating the risk of customer loans based on: The financial assets and earning
capacity of the borrower

 The prevailing economic climate

Improve operational efficiencies and boost profit

 Generate massive internal efficiencies (eg: analyzing the performance of sales personnel,
tellers and account managers)

 Track individual revenue streams to determine profitable and non-profitable services and
products

 Understand growth patterns to maximize the chance of repeatability

 Set key benchmarks for crucial metrics such as the number of net new customers and their
profitability, compare them against industry standards, and track them towards defined
goals

Customer segmentation

 Required to defined profitability amount of service and attention to be provided to customer

 Better understand customer needs and sentiments regarding banking


 Effective tailored product and services to a segment

 Effective customer profiling according to the segment

 Determine profitability across branches and products

 Identify and develop new cross-sell and up-sell opportunities and marketing campaigns
accordingly

Pushing new product to existing customers

 Need to maximize profit by cross selling and upselling of product to existing customers

 Cost of selling product to existing customer is five times lower than selling to new customer

 Improve customer relationship and customer loyalty

 Effectively and efficiently satisfy customer needs and demands

 Their latent needs have to be gauged and then should be approached with relevant product

Securing Existing client / Reducing churn rate

 Utmost attention to maintain customer relationship and

 Uncovering the reasons behind customers switching to a competing institution

 Tracking changes in customer behavior so products of services can be tailored accordingly

 Segregate customers into the baskets and focus on the needs of most profitable customers

Regulatory requirement

 Regulatory requirements indicated by the RBI for preparation of Off-site Monitoring
Surveillance (OSMOS) Reports on a regular basis in electronic format

 Asset Liability Management (ALM) guidelines for banks being implemented by the RBI w.e.f.
April 1, 1999

 Regulatory requirement of filing of statutory returns such as the one under Section 42 of the
Reserve Bank of India Act, 1934

 Need for timely submission of Balance Sheets and Profit & Loss Accounts

 Need for Inter-Branch Reconciliation of Accounts within a definite time frame

The KPI’s in retail banking:

KPIs are at the heart of a performance management initiative, and are meant to provide strategic
measures of success rather than just measuring non-critical activities and processes. KPIs can
provide “business alignment” across all levels of an organization (business units, departments and
individuals) with clearly defined and “cascaded targets” and benchmarks to create accountability


and track progress. That's why the success of any performance management program is dependent
on an effective strategy for defining, tracking and acting upon KPIs.

The KPI in retail banking may include the factors that have links to the performance of a retail bank.
There may be several KPI to measure the performance. However, it is important to keep the number
of KPI to a minimum and to choose KPI's that have direct attributes to its performance

 The total cash deposits held in a month

 The average annual deposits held

 Average number of depositors per retail bank branch

 Average withdrawals made by each depositor

 Ratio of active depositor to dormant depositor

 Average number of default borrowers in a year

 Average number of credit cards issued by the retail bank

 Rate of borrowing risk

 Rate of default risk

 Average number of customers served in a day

 Average number of closed bank accounts


Banking Data warehousing structure

Reference: http://www.ibm.com

The bank’s initial goal is to build the capability to better analyze the bank’s accounts. Users want the
ability to slice and dice individual accounts, as well as the residential household groupings to which they
belong. One of the bank’s major objectives is to market more effectively by offering additional products
to households that already have one or more accounts with the bank. After conducting interviews with
managers and analysts around the bank, we develop the following set of requirements:

1. Business users want to see 5 years of historical monthly snapshot data on every account.
2. Every account has a primary balance. The business wants to group different types of accounts in the
same analyses and compare primary balances.
3. Every type of account (known as products within the bank) has a set of custom dimension attributes
and numeric facts that tend to be quite different from product to product.
4. Every account is deemed to belong to a single household. There is a surprising amount of volatility in
account-household relationships due to changes in marital status and other life-stage factors.
5. In addition to the household identification, users are interested in demographic information as it
pertains to both individual customers and households. In addition, the bank captures and stores behavior
scores relating to the activity or characteristics of each account and household.

Based on further study of the bank’s requirements, we ultimately choose the following dimensions for
our initial schema: month end date, account, household, branch, product, and status. we take a monthly
snapshot and record the primary balance and any other metrics that make sense across all products, such
as interest paid, interest charged, and transaction count. Remember that account balances are just like
inventory balances in that they are not additive across any measure of time. Instead, we must average
the account balances by dividing the balance sum by the number of months.


Fact table for all accounts (source: The data warehouse toolkit: Ralph kimbal, Margy ross)

Multivalue dimensions:

An account can have one, two, or more individual account holders, or customers, associated with it.
Obviously, we cannot merely include the customer as an account attribute; doing so violates the
granularity of the dimension table because more than one individual can be associated with an account.
Likewise, we cannot include customer as an additional dimension in the fact table; doing so violates the
granularity of the fact table (one row per account per month) again because more than one individual
can be associated with any given account. In some financial services companies, the individual customer
is identified and associated with each transaction. For example, credit card companies often issue unique
card numbers to each cardholder. John and Mary Smith may have a joint credit card account, but the
numbers on their respective pieces of plastic are unique. In this case there is no need for an account-to-
customer bridge table because the atomic transaction facts are at the discrete customer grain. Account
and customer would both be foreign keys in this fact table

Multivalue account table (source: The data warehouse toolkit: Ralph kimbal, Margy ross)


Arbitary band value reporting
Suppose that business users want the ability to perform value-band reporting on a standard numeric fact,
such as the account balance, but are not willing to live with predefined bands. The band definition table
can contain as many sets of different reporting bands as desired. The name of a particular group of bands
is stored in the band group column. The band definition table is joined to the balance fact using a pair of
less-than and greater than joins. The report uses the band range name as the row header and sorts the
report on the band sort column

Arbitarty band value reporting (source: The data warehouse toolkit: Ralph kimbal, Margy ross)

Point in time balance

So far we’ve restricted our discussions in this financial services chapter to month-end balance snapshots
because this level of detail typically is sufficient for analysis. If required, we could supplement the
monthly-grained snapshot fact table with a second fact table that provides merely the most current
snapshot as of the last nightly update or perhaps is extended to provide daily-balance snapshots for the
last week or month. However, what if we face the requirement to report an account’s balance at any
arbitrarily picked historical point in time?. Assuming that business requirements already have driven the
need to make transaction detail data available for analysis, we could leverage this transaction detail to
determine an arbitrary point-in-time balance. To simplify matters, we’ll boil the account transaction fact
table down to an extremely simple design, as illustrated in Figure. The transaction type key joins to a
small dimension table of permissible transaction types. The transaction sequence number is a
continuously increasing numeric number running for the lifetime of the account. The final flag indicates
whether this is the last transaction for an account on a given day. The transaction amount is self-
explanatory. The balance fact is the ending account balance following the transaction event.


Point In time table (source: The data warehouse toolkit: Ralph kimbal, Margy ross)

Heterogeneous product: Specific line of business schema

A typical retail bank offers a myriad of dissimilar products, from checking accounts to mortgages, to the
same customers. Although every account at the bank has a primary balance and interest amount
associated with it, each product type has a number of special attributes and measured facts that are not
shared by other products. For instance, checking accounts have minimum balances, overdraft limits, and
service charges; time deposits such as certificates of deposit have few attribute overlaps with checking
but instead have maturity dates, compounding frequencies, and current interest rate. Business users
typically require two different perspectives that are difficult to present in a single fact table. The first
perspective is the global view, including the ability to slice and dice all accounts simultaneously,
regardless of their product type. This heterogeneous product technique obviously applies to any business
that offers widely varied products through multiple lines of business. If we worked for a technology
company that sells hardware, software, and services, we can imagine building core sales fact and product
dimension tables to deliver the global customer perspective. The core tables would include all facts and
dimension attributes that are common across lines of business. The core tables would then be
supplemented with schemas that do a deep dive into custom facts and attributes that vary by business.
Again, a specific product would be assigned the same surrogate product key in both the core and custom
product dimensions.

Heterogeneous product table(source: The data warehouse toolkit: Ralph kimbal, Margy ross)


Few Examples of business intelligence in Indian banking sector

Bank of India
Bank of India (BOI) is one of India's largest public sector banks with over 2500 branches across India,
and a network of 21 branches at key financial centers across 10 countries. With total deposits of
approx. US$12bn, the Bank ranks 5th in the country (as per a recent study by Business Today). Bank
of India's mission is to provide superior, proactive banking services to niche markets globally, while
providing cost-effective, responsive services to others in its role as a development bank, while
meeting the
requirements of its stakeholders. BOI has branches for Commercial & Personal Banking, Corporate
Banking, Overseas Banking, Capital Market, Merchant Banking and specialized Branches for Asset
Recovery, Small Scale Industries, Hi-tech Agriculture Finance, Lease Finance and Treasury.

Source: http://www.hp.com
ICICI Bank

ICICI Bank is India's largest private sector bank. The bank has a network of 1,308 branches and 3,950 ATMs as
well as robust Internet banking. The Bank is present in 19 countries, including India. ICICI Bank offers a wide
range of banking products and financial services to corporate and retail customers through a variety of
delivery channels and through its specialized subsidiaries and affiliates in the areas of investment banking, life
and non-life insurance, venture capital and asset management.

Previously, the bank was dependent on its DW from Teradata. With dramatic growth in its users, amount of
data, and source stations, etc., the increasing cost of scaling and maintenance and mounting system
unavailability posed difficulties for the bank. To resolve these recurring problems, the bank undertook a
migration of the enterprise data warehouse from Teradata to Sybase IQ. The success of this project provides
the bank with an always available system, visibly increased query performance, and lower TCO among a host
of many other benefits.


Business Advantage

 ICICI bank has achieved tremendous improvement in system uptime and significant
improvement in query performance over its previous Teradata implementation, in addition
to the host of other benefits of the Sybase IQ data warehouse migration.

Key Benefits

 Compresses data by over 60%
 Leverages scalability owing to its open system architecture
 Achieves trickle-feed loading
 Allows for simultaneous loading and querying
 Supports more than 150 users concurrently
 Reduces downtime by providing 24x7 availability
 Significantly improves query performance and response time
 Lowers cost of maintenance and TCO
 Supports heterogeneous environment as it is hardware and platform independent


References:
1. JAYANTHI RANJAN, BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS,
TECHNIQUES AND BENEFITS, Journal of Theoretical and Applied Information Technology, 2009
2. Ralph Kimbal, Margy Ross , The Data Warehousing Toolkit: The complete guide to dimension modeling, 2nd
edition, Wiley
3. www.ibm.com
4. www.sybase.in
5. www.hp.com
6. http://www.wikipedia.org/
7. http://www.hexaware.com
8. http://www.elegantjbi.com
9. http://business.mapsofindia.com

Busienss intelligence in banking sector

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