Business Model Canvas (BMC)- A new venture concept
Business Intelligence at Punjab National Bank
1. Business Intelligence at Punjab National Bank
A Business Intelligence Project
Business Intelligence at Punjab
National Bank
GROUP 1
ManishArora 201071
Neharika Mallick 201086
Puneet Arora 201111
Raashi Sodhi 201112
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EXECUTIVE SUMMARY
In the past decade, developments in the field of information technology (IT) have strongly
supported the growth and inclusiveness of the banking sector by facilitating inclusive economic
growth. The industry has come a long way from introduction of credit cards in 90s to new
transaction and analytical systems in 2012.Today banks are storing more information than ever.
Bankers must have the right information at the right time helping them making more informed
and intelligent decisions.
The main objective of the project was to study the implementation of Data Warehouse System
in PNB (Punjab National Bank). Needs for implementation of Data Warehouse were identified.
The CVC deadline to computerize 70 % of its business being the main driver for the initiative
proved to be a blessing in disguise for efficient operations of PNB. Major challenges for
implementing the new system were studied.
PNB had certain requirements which were not being fulfilled by the existent systems like a
unified view of data, timely compilation, monitoring of weak areas, adherence to statutory
reporting requirements and structured analysis of data for information decision making. The
Enterprise wide Data Warehouse (EDW) project was initiated by the Bank for leveraging the
Bank's operational data available in multiple source systems to facilitate ready access to data
required for regulatory, statutory reporting and for various other analytical purposes.
During the project PNB faced several issues like data quality, data extraction, data loading, data
loading, CRM. The issues faced during implementation process were successfully overcome.
The bank undertook a data cleansing exercise which is an ongoing activity and is being
conducted through concentrated efforts by the Bank. The EDW project implementation was
carried out in a phased manner, with separate timelines for various solutions such as MIS, Risk
Management, Anti Money Laundering, Customer Relationship Management, ALM and Funds
Transfer Pricing.The EDW solution successfully provided an integrated solution for Risk
Management, Anti-money laundering, and Customer Relationship management for enterprise
wide users. The implementation of the data warehouse has not only given PNB better control and
insight into its operations; it’s also given management the perspective it requires to achieve the
bank’s vision.
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TABLE OF CONTENTS
EXECUTIVE SUMMARY ........................................................................................................... 2
TABLE OF FIGURES ................................................................................................................. 4
CHAPTER1: BANKING INDUSTRY: INTRODUCTION .................................................................. 5
1.1 Structure Of Indian Banking Industry .........................................................................................5
1.2 Challenges Faced By Indian Banking Industry .............................................................................6
1.3 IT In Banking Sector ..................................................................................................................7
1.4 Data Warehousing In Banking Sector .........................................................................................8
CHAPTER 2: PUNJAB NATIONAL BANK: COMPANY PROFILE .................................................. 11
CHAPTER 3: PNB: THE BEGINNING OF IT STRATEGY .............................................................. 13
3.1 SWOT Analysis ........................................................................................................................ 13
3.2 IT Strategy .............................................................................................................................. 14
3.2.1 Short Term Goal ......................................................................................................................... 14
3.2.2 Hardware and Training .............................................................................................................. 14
3.2.3 Long-term strategy..................................................................................................................... 15
CHAPTER 4: CORE BANKING ARCHITECTURE......................................................................... 15
4.1 Culture and technology issues ................................................................................................. 16
4.2 Systems .................................................................................................................................. 16
4.3 Network design ...................................................................................................................... 16
4.4 Storage systems...................................................................................................................... 17
4.5 Initiatives ............................................................................................................................... 17
CHAPTER 5: ENTERPRISE WIDE DATA WAREHOUSE: PLANNING ............................................ 18
5.1 Requirements ......................................................................................................................... 19
5.2 Reasons for choosing EDW ...................................................................................................... 20
5.3 Challenges during Implementation Phase ................................................................................ 21
5.4 Solution Provided for various Business needs .......................................................................... 23
5.4.1 MIS and Analytics: ............................................................................................................... 23
5.4.2 Customer Relationship Management:................................................................................... 23
5.4.3 Risk Management: ............................................................................................................... 24
CHAPTER 6: ENTERPRISE DATA WAREHOUSE SOFTWARE ..................................................... 25
6.1 Scope ..................................................................................................................................... 25
6.2 Benefits .................................................................................................................................. 26
6.3 Salient features of this project: ............................................................................................... 27
CHAPTER 7: FUTURE SCOPE ................................................................................................. 28
REFERENCES ........................................................................................................................ 30
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TABLE OF FIGURES
Figure 1.1: Indian Banking Structure.............................................................................................. 5
Figure 1.2: Banking industry performance ..................................................................................... 6
Figure 1.3: Major banking products and vendors ........................................................................... 7
Figure 1.4: Data Warehouse structure ............................................................................................ 9
Figure 3.1: SWOT Analysis .......................................................................................................... 13
Figure 5.1: Project Specs .............................................................................................................. 21
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CHAPTER1: BANKING INDUSTRY: INTRODUCTION
The banking industry in India has a huge canvas of history, which covers the traditional banking
practices from the time of Britishers to the reforms period, nationalization to privatization of
banksand now increasing numbers of foreign banks in India.. Banking in India originated in the
last decades of the 18th century. The first banks were The General Bank of India, which started
in 1786, and Bank of Hindustan, which started in 1770; both are now defunct. The oldest bank in
existence in India is the State Bank of India, which originated in the Bank of Calcutta in June
1806. It was one of the three presidency banks, the other two being the Bank of Bombay and the
Bank of Madras. The three banks merged in 1921 to form the Imperial Bank of India, which,
upon India's independence, became the State Bank of India in 1955.
1.1 Structure Of Indian Banking
Industry
Banking Industry in India functions under the
sunshade of Reserve Bank of India - the
regulatory,central bank. Banking Industry
mainly consists of:
Commercial Banks
Co-operative Banks
The commercial banking structure in India
consists of:
Scheduled Commercial Banks
Unscheduled Bank.
Scheduled commercial Banks constitute those
banks which have beenincluded in the Second
Schedule of Reserve Bank of India (RBI) Act,
1934.
Figure 1.1: Indian Banking Structure
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Banking industry in India has also achieved a new height with the changing times. The use
According to a Mckinsey report, the Indian banking sector is heading towards being a high-
performing sector.
Figure 1.2: Banking industry performance
According to an IBA-FICCI-BCG report titled ‘Being five star in productivity – road map for
excellence in Indian banking’, India’s gross domestic product (GDP) growth will make the
Indian banking industry the third largest in the world by 2025. According to the report, the
domestic banking industry is set for an exponential growth in coming years with its assets size
poised to touch USD 28,500 billion by the turn of the 2025 from the current asset size of USD
1,350 billion (2010)”.
1.2 Challenges Faced By Indian Banking Industry
Developing countries like India, still has a huge number of people who do not have access to
banking services due to scattered and fragmented locations. But if we talk about those people
who are availing banking services, their expectations are raising as the level of services are
increasing due to the emergence of Information Technology and competition. Since, foreign
banks are playing in Indian market, the number of services offered has increased and banks have
laid emphasis on meeting the customer expectations.
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1.3 IT In Banking Sector
Information technology is one of the most important facilitators for the transformation of the
Indian banking industry in terms of its transactions processing as well as for various other
internal systems and processes. The various technological platforms used by banks for the
conduct of their day to day operations, their manner of reporting and the way in which interbank
transactions and clearing is affected has evolved substantially over the years.
1.3.1 Technological Development in Banks:
Developments in the field of information technology (IT) strongly supports the growth and
inclusiveness of the banking sector by facilitating inclusive economic growth .IT improves the
front end operations with back end and helps in bringing down the transaction costs for the
customers.
Important events in India:
Arrival of card-based payments- Debit, Credit card late 1980s and 1990s
Introduction of Electronic Clearing Services (ECS) in late 1990s
Introduction of Electronic Fund Transfer (EFT) in early 2000s
Introduction of RTGS in March 2004
Introduction of National Electronic Fund
Transfer(NEFT) as a replacement to Electronic Fund
Transfer/Special Electronic Fund Transfer in 2005/2006
Cheque transaction System (CTS) in 2007
Figure 1.3: Major banking products and vendors
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Data warehouse and mining: Banks are storing more information than ever before. Decision
makers must have the right information at the right time to help them make more informed and
intelligent decisions. The data in the operational database represents current transactions,
however the decisions are based on a different time frame; that is there is no time component. On
the other hand, data in operational databases are stored with a functional or process orientation,
what really decision-makers would like to have is subject orientation of data, which facilitates
multiple views for data and decision making. Data Warehousing and Data Mining are the right
solution that makes the above possible. Use of Data Mining tools is being done for customer
segmentation and profitability, marketing and customer relationship management
Banks need to optionally leverage technology to increase penetration, improve their productivity
and efficiency, deliver cost-effective products and services, provide faster, efficient and
convenient customer service and thereby, contribute to the overall growth and development of
the country. Technology enables increased penetration of the banking system, increases cost
effectiveness and makes small value transactions viable. Besides making banking products and
services affordable and accessible, its simultaneously ensures viability and profitability of
providers.
1.4 Data Warehousing In Banking Sector
Data warehousing and data mining are relatively new terms for banking sector. These terms
have gained significance with the growing sophistication of technology and the need for
predictive analysis with What if simulations. MIS in the present context of high availability of
voluminous data on electronic media at diverse locations and on diverse platforms, has become
more pertinent to banks’ decision-making process, thanks to the availability of new tools of
technology such as data warehousing, data mining.
Data warehousing which refers to collection of data from various sources (internal and external)
and placing them in a form suitable for further processing which will gain critical importance in
the presence of data mining which refers to the process of extracting hidden information and
generating several types of analytical reports which are usually not available in the original
transaction processing systems.
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1.4.1 Relevance of Data Warehousing and Data Mining for banks in India
Banking being an information intensive industry, building a Management Information System
within a bank or an industry is a gigantic task. It is more so for the public sector banks which
have a wide network of bank branches spread all over the country. It becomes all the more
difficult due to prevalence of varying degrees of computerisation. At present, banks generate
MIS reports largely from periodic paper reports/ statements submitted by the branches and
regional/zonal offices. Except for a few banks which have been using technology in a big way,
MIS reports are available with a substantial time lag. Reports so generated have also a high
margin of error due to data entry being done at various levels and the likelihood of varying
interpretations at different levels.
Figure 1.4: Data Warehouse structure
The implication of adopting such technology in a bank would be as under:
1) All transactions captured at the branch level would get consolidated at a central location.
Such a central location could be called the Data Warehouse of the concerned bank. For
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this to happen, one of the requirements would be to establish connectivity between the
branches on the one hand and the Data Warehouse platform on the other.
2) For banks with large number of branches, it may not be desirable to consolidate the
transaction details at one place only. It can be decentralised by locating the services on
regional basis. The regional Data marts as developed can provide mutual back-up and
could be linked to the central Data Warehousing server so that for the purpose of MIS at
the corporate level, data can be accessed from all the regional Data marts.
3) By way of data mining techniques, data available at various computer systems can be
accessed and by a combination of techniques like classification, clustering, segmentation,
association rules, sequencing, decision tree. Various ALM reports such as Statement of
Structural Liquidity, Statement of Interest Rate Sensitivity etc. or accounting reports like
Balance Sheet and Profit & Loss Account can be generated instantaneously for any
desired period/date.
4) Significant cost benefits, time savings, productivity gains and process re-engineering
opportunities are associated with the use of data warehouse for information processing.
Data can easily be accessed and analysed without time consuming manipulation and
processing. Decisions can be made more quickly and with confidence that the data are
both time-relevant and accurate. Integrated information can be also kept in categories that
are meaningful to profitable operation.
5) Trends can be analysed and predicted with the availability of historical data and the data
warehouse assures that everyone is using the same data at the same level of extraction,
which eliminates conflicting analytical results and arguments over the source and quality
of data used for analysis. In short, data warehouse enables information processing to
be done in a credible, efficient manner.
Some of the data warehouses available in market areExadata (Oracle), TwinFin (Netezza/IBM),
DB2 (IBM), SQM (Microsoft) etc.
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CHAPTER 2: PUNJAB NATIONAL BANK: COMPANY
PROFILE
Punjab National Bank (PNB) is an Indian financial services company based in New Delhi, India.
PNB is the third largest bank in India by assets. It was founded in 1894 and opened for business
on 12 April, 1895. It is currently the second largest state-owned commercial bank in India ahead
of Bank of Baroda with about 5000 branches across 764 cities. The bank has been ranked 248th
biggest bank in the world by the Bankers Almanac, London. The bank's total assets for financial
year 2007 were about US$60 billion. PNB has a banking subsidiary in the UK, as well as
branches in Hong Kong, Dubai and Kabul, and representative offices in Almaty, Dubai, Oslo,
and Shanghai. PNB has the distinction of being the first Indian bank to have been started solely
with Indian capital that has survived to the present.
With over 72 million satisfied customers and 5697 domestic branches, PNB has continued to
retain its leadership position amongst the nationalized banks. The Bank enjoys strong
fundamentals, large franchise value and good brand image. Over the years PNB has remained
fully committed to its guiding principles of sound and prudent banking irrespective of conditions.
Bank has been earning many laurels and accolades in recognition to its service towards doing
good to society, technology usage and on its overall performance.
Vision: "To be a Leading Global Bank with Pan India footprints and become a household brand
in the Indo-Gangetic Plains providing entire range of financial products and services under one
roof".
Mission:"Banking for the unbanked".
Awards: Some of the major awards won by the Bank are the Best Bank Award, Most Socially
Responsive Bank by Business World-PwC, Most Productive Public Sector Bank, Golden
Peacock Awards by Institute of Directors, etc.
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Services Offered:
Savings Fund Account Doorstep Banking Services
Current Account Cards
Fixed Deposit Schemes Nomination Facilities
AUTO RENEWAL Deceased claim cases
Credit Schemes Centralised Banking Solution
Capital Gain Account Scheme-1988 View Your Loan Application Status
Growth:
Profit: Company posted a 12.7 per cent rise in net profit to Rs 1,246 crores during the first
quarter of the 2012-13 fiscal year due to growth in interest income.
Business: Total Business of the Bank reached Rs. 673363 crores as against Rs. 5,55,005 crores
in March 2011, showing a y-o-y growth of 21.3%.
Delivery Channels:
Bank’s branch network stands at 5670 (including 6 extension counters).
Bank has 6009 ATMs and around 169 lakh card holders.
PNB Internet Banking Channels are witnessing a steady increase in usage with about 17
lakh internet banking users.
Future Goal: The bank plans to gross a total business of Rs 10 lakh crores by 2013. It aims to
increase its customer base to 150 million by 2013, as per PNB chairman and managing director
K R Kamath (Economic Times, Jan 30, 2011). Company wants to expand its global operations
and has started by upgrading its Norway based office.
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CHAPTER 3: PNB: THE BEGINNING OF IT STRATEGY
Back in 2003, Punjab National Bank used a two-pronged strategy to IT-enable itself and support
present and future business needs. Earlier, Only 35 % of the bank's business was computerized
and a number of smallsoftware packages ran on standalone PCs. In March 2000, the penetration
and use of IT was not very high at PNB. The bank used seven different software systems, which
ran on 13 different flavors of UNIX, on standalone PCs. The 500-odd branches were not
networked and only 35 percent of the bank's business was computerized. The overall expertise in
IT among users was low.The Central Vigilance Commission (CVC) issued a directive to the
bank to computerize at least 70 percent of its business by December 2000. This prompted the
bank to work out a strategy to tackle the daunting task in the short period of time.
3.1 SWOT Analysis
STRENGTHS WEAKNESSES
1) The bank personnel would be able to readily embrace 1) Different Unix OS flavors in different branches.
the use of IT.
2) Different standalone financial applications on PCs at
2) An existing pool of qualified knowledge-based different branches.
personnel would contribute largely to the IT initiatives.
3) Lack of interoperability due to disparity in systems.
3) The financial position of the bank was very sound.
4) Limited expertise on the software packages currently
There would not be any constraint of funds to facilitate IT
deployed. This increased dependence on vendors.
initiatives.
5) Systems audits were pending.
4) The bank wasn't bound to too much legacy systems and
equipment. 6) Most branches did not have a proper LAN in place.
7) There was almost no WAN connectivity.
SWOT
OPPORTUNITIES
1) More control through Dashboard for Senior
Management covering all KPIs related to THREATS
Deposits, Advances, Profits, NPAs, etc
2) Data Mining Infrastructure Capabilities for 1) Lack of continuous Support from Management
mathematical and statistical modelling to determine and
2) Lack of consistent data for implementing the project
predict correlation, patterns, and trends among a variety
of measures. 3) Lack of support from Managers to go online and use of
new technology
3)Compete more effectively with Private players through
Customer Analytics covering Customer Profiling, Customer
Segmentation, Lead Analysis & Cross Sell Analysis
Figure 3.1: SWOT Analysis
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3.2 IT Strategy
In 2000, to tackle the problem, PNB hired a consultant and devised a two-pronged plan of action.
The plan comprised:
1. A short term goal - To meet the CVC deadline of 70 percent computerization.
2. A long term goal - To create a dependable core banking infrastructure and build a
nationwide network to connect different branches to the core infrastructure.
3.2.1Short Term Goal
In order to meet the CVC deadline the bank decided to deploy simple IT infrastructure so that it
could computerize 70 percent of its business within the deadline. The IT team decided to
implement an application, which could run on standalone PCs across its nationwide branches.
The application vendor would have to provide nationwide support since the in-house IT team
could not provide support at all branches.
PNB chose a product from a company called Nelito. It was a DOS-based, 'Partial Branch
Automation' application. Standalone versions were chosen since there weren't LANs in place,
and deployment of LANs at branches would take so long that the CVC deadline couldn't be met.
The interface was simple in design, and thus easy for the bank personnel to use.
3.2.2Hardware and Training
The bank selected two hardware vendors and the application software was embedded into the
hardware to make them 'plug-and-play' capable. Nelito's package was deployed at one branch at
a time. And after each successful implementation at a branch, it was replicated at a newer
branch.
Internal training sessions for the bank personnel were conducted with the help of 14 training
institutes. The source code of the product was tweaked to facilitate deployment. The IT team was
specially trained to re-architect the source code, and make any modifications, improvements,
value additions, and enhancements. Deployment at the selected branches was over by December
2000.
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The bank requested CVC for an extension of the deadline and was granted time till March 2001.
By March 2001, 70.60 percent of the bank's business was computerized.
3.2.3Long-term strategy
In the long-term, PNB wanted a technology that would consolidate all its business resources and
sustain the bank's future growth. It also wanted to create its own network, which would play a
vital role in its success. Three consultants were appointed to review technology options for long-
term adoption. The verdict of the consultants was to deploy a centralized core banking
architecture.
CHAPTER 4:CORE BANKING ARCHITECTURE
On 30 March 2001, the bank used the services of Infosys for the deployment of
Finnacle.Finnacle is a software package consisting of universal banking products which are
designed to address the core banking, e-banking, Islamic banking, treasury, wealth
management and CRM requirements of retail, corporate and universal banks. It is developed
by Infosys, and is one of the major players in the arena of core banking in Indian and Asian
banking domains.
PNBselected a core team, which would be the heart of the project. Infosys trained 200-odd
personnel from a core team over six months. The core team modified and customized the
package according to its specific needs.
It was then time to procure hardware. PNB purchased servers, security infrastructure, and storage
equipment and decided to house it in its own central data center in New Delhi. A lot of
infrastructure from Cisco has been used to build the data center.
In April 2002 the bank rolled-out Finnacle in seven branches as a pilot venture. This was done
because the bank had seven different application packages, and it wanted to ensure smooth
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migration of the data into Finnacle. By mid May 2002, all data from other software was
successfully migrated into Finnacle.
4.1 Culture and technology issues
PNB faced issues which were mostly cultural. Most staffers were used to working in a manual
environment, and some had worked in standalone environments. In the new networked
environment, personnel at the node/counter didn't actually 'see' the transactions updating in the
various account books.
This gave rise to a number of queries and suggestions from personnel. The bank consulted
IDRBT(Institute for Development & Research in Banking Technology) and RBI to verify the
implementation success and it was reported that the deployment was absolutely correct. Around
six months later, the personnel felt that the environment 'change' had done them good, and was
used to working on the systems.
There were a few integration issues when migrating to Finnacle, but the in-house IT team was
able to resolve them all. The pilot for the initial seven branches was a test-bed for PNB. The
knowledge we gained from the pilot deployments helped it overcome the future issues.
4.2 Systems
Before deploying the core banking architecture, PNB used servers which were NT-based, from
IBM, and from other vendors. The bank conducted benchmarking tests for Finnacle on various
server platforms. And it was satisfied with the performance of Sun's hardware on Solaris. Sun's
Fire servers, Solaris OS, and Oracle's RDBMS are now in use.
4.3Network design
Cisco tied up with PNB to evolve the network design and implement a nationwide network
backbone to connect all its offices. Cisco assisted the bank in understanding and implementing
the various technologies associated with the project. The converged network infrastructure
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allowed PNB to standardize the applications and software needed to provide the banking
services.
4.4 Storage systems
The bank has followed RBI's storage requirement guidelines. Provisions have been made to store
transaction data for around 10 years. In some cases, data is stored permanently. Around 164 Sun
enterprise class servers are used in DAS architecture. The total capacity is of multiple TBs.
4.5 Initiatives
These are some initiatives the bank decided to undertake in future:
Set up a data warehouse and a data mart. IDRBT has been involved as a consultant.
It may need to set up a NAS and SAN to consolidate its storage.
Disaster Recovery site may be built at Mumbai to create a replica of its data center. It will
take around six months to be functional.
A call center will be set up as a CRM initiative, which uses information from the data
warehouse with the help of the Base24 switch
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CHAPTER
5:ENTERPRISE WIDE
“Operational efficiency has been one of
DATA WAREHOUSE: the key benefits of this implementation.”
The project has plugged revenue leaks in
PLANNING
PNB’s system which Misra conservatively
estimates in the range of Rs 10 Crore.
Punjab National Bank (PNB) is the
third largest bank in India with a
presence in nine countries. PNB has
more than 5,200 Service outlets
connected through a Centralized Core Banking solution. It has global business of more than Rs
4, 50,000 crores and serves over 37 million customers. PNB has continued to retain its leadership
position among the nationalized banks. The bank enjoys strong fundamentals, large franchise
value and good brand image. Besides being ranked as one of India's top service brands, PNB has
remained fully committed to its guiding principles of sound and prudent banking.
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5.1 Requirements
Punjab National Bank (PNB) had certain requirements which were not being fulfilled by the
existent system:
A unified view of business-related data.
Timely data compilation.
Timely monitoring and reporting of compliance.
Adherence to statutory reporting requirements.
Steps to prevent money laundering as per BASEL committee specifications.
Structured analysis of data for informed decision-making.
Monitoring of weak performance areas.
Improved customer service.
CRM with customer profiling and segmentation.
Support of the launch of new products and services.
An integrated source to feed in various downstream point solutions which require
complex data processing.
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5.2 Reasons for choosing EDW
The Enterprise wide Data Warehouse (EDW) project was initiated by the Bank for leveraging the
Bank's operational data available in multiple source systems to facilitate ready access to data
required for regulatory, statutory reporting and for various other analytical purposes. This also
helped in achieving operational efficiency and enhanced business decision support at various
levels of the Bank. The EDW project also aimed at enabling PNB to meet business challenges
such as Basel II compliance for Risk Management, increase profitability through Customer
Relationship Management solution and implementation of Anti Money Laundering safeguards as
per the regulatory guidelines.
The project was implemented by Tata Consultancy Services Ltd. (TCS) on turnkey basis. In
order to ensure smooth implementation of the project, it was being implemented in a phased
manner. There was no impact on the functioning of the Bank during the implementation of the
project.
The scale and complexity of the EDW project, which involved addressing the MIS and analytical
requirements of 39 divisions and in addition to implementing complex analytical solutions made
it extremely challenging.
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Project Specs
Deployment Location: NewDelhi
Team Size: 32
Tech Used: DB2 UDB, M1(Data Modeling),
Data Stage, IBM-AIX, SAP-Business
Objects, IBM Websphere, IBM p5 Series
Servers on AIX, IBM 3800 Series & 3900
series Windows Servers
Expected life: 8 years
Figure 5.1: Project Specs
5.3 Challenges during Implementation Phase
Since its humble beginnings in 1895 with the distinction of being the first Indian bank to have
been started with Indian capital, PNB has achieved significant growth in business. PNB is
currently ranked as the 3 largest bank in the country (after SBI and ICICI Bank) and has the
2ndlargest network of branches.
The technical challenges faced by PNB were as follows:
1) Addressing issue of data quality: A bank wide drive for cleansing of MIS master data,
as well as the mapping of EDW master codes with the corresponding asset class, was
initiated at branch level in a time bound manner. The data received from source systems
often had unwanted characters or junk records, for which special Reject Handling
routines have been implemented.
2) Data extraction challenges: Since data was extracted from various sources system, with
their respective servers located at multiple locations, it required complex coordination
with various divisions, for ensuring availability of various operational source systems
was a challenge - in order to ensure that there is no disruption, data extraction needs to be
carried out in a very small time window. The extraction of CBS data was done on daily
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basis from designated CBS server which is used for MIS purpose by the Bank. Since this
server was accessed by about Bank. Since this server was accessed by about 2000+
branches for generating various MIS reports, apart from testing of new/customized CBS
as such there was considerable load on the server. The situation worsened during the
month/quarter ends when there was heavy utilization of servers. The available time
window during such situation was few hours during which data for EDW solution was
extracted. Data was extracted from multiple, disparate source system which had different
data extraction frequency. Maintaining account level details for data coming from two
different source systems at different time interval was also a challenge.
3) Data Loading challenges: Data transformation and loading is performed through IBM
DataStage. Data loading of daily incremental data is done in three stages, taking about 8
hours. Ensuring smooth and timely loading of data, so as not to affect the business users,
required concentrated effort by the data loading team. Pipeline parallelism and partition
parallelism features of DataStage were implemented successfully for processing massive
volume of data. Also at database level, Distributed Partitioning Feature (DFP) of DB2
has been implemented for meeting performance challenges. The use of LOAD utility
instead of WRITE Utility improved the performance 11 folds for Bulk Load activities
(especially during Historical Data Load). Special care was taken to handle Job Aborts in
Bulk Load activities, to ensure that data load did not start afresh. During Bulk Load and
Historical Data Load, Server overload due to limitations of Number of connections to
DataStage was addressed as Data loading was being carried out 24x7
4) Integration of Customer Data Quality tool with the daily ETL Load: The challenge
was in ensuring bi-way data flow between the ETL subsystem and the Customer Data
Quality tool, to ensure that no time was lost in data transfer from one system to another.
This has been achieved by integrating windows scripts with the ETL jobs through event
driven synchronization
5) Point Solutions Integration: Format of data requirements of point solutions vary from
flat files, tables to xml files. Challenges in meeting size limitations of xml files have been
met by using Parallelism.
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6) Customer Relationship Management (CRM): Information of prospective customers
was not captured hence the possibility of converting such leads into actual business was
very marginal.
The issues faced during implementation process were successfully overcome. Ensuring clean
data in source systems is critical to the success of the EDW solution. The bank undertook a data
cleansing exercise which is an ongoing activity and is being conducted through concentrated
efforts by the Bank.The EDW project implementation was carried out in a phased manner, with
separate timelines for various solutions such as MIS, Risk Management, Anti Money
Laundering, Customer Relationship Management, ALM and Funds Transfer Pricing.
5.4 Solution Provided for various Business needs
5.4.1MIS and Analytics:
Enterprise-wide Logical Data Model spanning Financial and Non-Financial Data
Elements of the Bank to cover all MIS and DSS needs
MIS and DSS Requirements covering Retail Banking, International Banking, Credit
Administration, Special Assets Management, Priority Sector and Lead Banking,
Inspection and Audit, Merchant Banking, HR and Others
Financial Consolidation – Balance Sheet, Profit/Loss, Revenue
Dashboard for Senior Management covering all KPIs related to Deposits, Advances,
Profits, NPAs, Priority Sector, Branch Profitability, Employee Performance across
dimensions like Product, Industrial Sector, Customer, Organisation and Time
Data Mining Infrastructure Capabilities for mathematical and statistical modeling to
determine and predict correlation, patterns, and trends among a variety of measures.
5.4.2 Customer Relationship Management:
Transactional CRM covering Lead Management,
Activity Management, Campaign Management, Mass Business Partner Generation,
Complaints Management, Integration with Alternate Delivery Channels like Call Centre
& ATMs
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Customer Analytics covering Customer Profiling, Customer Segmentation, Lead
Analysis & Cross Sell Analysis
5.4.3 Risk Management:
Credit Risk, Market Risk, Operational Risk
Asset Liability Management and Funds Transfer Pricing
Anti-Money Laundering
Alerts, Cases, Statutory and Regulatory Reporting.
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CHAPTER 6:ENTERPRISE DATA WAREHOUSE SOFTWARE
PNB implemented Enterprise Data Warehouse and point solutions to meet these requirements.
The software uses included
IBM DB2 Universal Data Enterprise – Server Edition – Version 9.1
IBM DB2 Data Warehouse – Enterprise Edition
IBM Tivoli Storage Manager – Extended Edition
IBM Tivoli Storage Manager – Storage Area Networks
IBM WebSphere DataStage Version 4.5.2
IBM WebSphere Application Server.
PNB’s Date warehouse solution had capabilities such as data extraction from source systems,
data modeling, data transformation and loading, reporting tools (queries and reports), and data
analytics mining. The data warehouse hardware operating system was IBM – AIX (Unix
operating systems).
6.1 Scope
2 million transactions processed through the data warehouse daily.
More than 10 source systems have been integrated and data is extracted and loaded on a
daily basis. More than 20 lakh transactions are processed, loaded in base tables and
summarized per day.
More than 350 reports have been published with drill down features for HO, circles and
branches.
More than 40 dashboard reports are available for focussed monitoring and decision
support of low-performing branches and circles. The reports feature convenient tools
such as growth graphs, growth comparisons in percentage terms, traffic lights and pie
charts.
The anti-money laundering solution has been implemented. More than 15 lakh
transactions are monitored and around 6,000 alerts have been generated for further
scrutiny. Suspicious transactions and cash transactions beyond the threshold limit are
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monitored and reported to statutory agencies as required. The system also facilitates
follow-up and closure of alerts.
A CRM system has been implemented in 1,024 branches.
An Operational Risk Management Solution (Operations Risk, Credit Risk and Market
Risk) has been implemented and operational risk data from all the branches and offices is
captured here. Risk assessment surveys are conducted online through the system.
Advanced approach for Operational Risk as per BASEL guidelines has been
implemented.
6.2 Benefits
The EDW project was a large and Complex
implementation. It has been a mammoth
exercise from many perspectives, be it the
volume of data , areas/user requirements
covered under the enterprise wide
implementation, or the number of users. The
enterprise wide implementation of EDW project
in a large PSU bank like Punjab National Bank
was unprecedented. The EDW solution
successfully provided an integrated solution for
Risk Management, Anti-money laundering, and
Customer Relationship management for
enterprise wide users. EDW provided an end to
end solution for Basel compliance for Risk
Management Division, covering Operational
Risk, Credit Risk and Market Risk. The Risk
Management solutions include solutions for
Credit Risk (Standardized Approach), FIRB, AIRB (for Operational Risk), BIA, TSA and AMA,
and for Market Risk (Standard Duration approach). Apart from this, Solutions for Transfer
pricing mechanism and Asset Liability Management is also being implemented.
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6.3 Salient features of this project:
1) Unique Collaborative and Participative approach between PNB, IBM and TCS: A unique
participative model between PNB, TCS and IBM has been setup to ensure successful
implementation at PNB.
2) Customized BDW usage for Indian Banking industry: The BDW model provided by IBM
has undergone customization in terms of adapting it to the Indian Banking scenario. The
process of such a customization involving Indian Banking uniqueness has been done the
first time in PNB.
3) Highly tuned and Scalable Infosphere DataStage Process: The InfosphereDataStage
implementation includes the best practices involved in tuning the job and sequences to
ensure load within the available window.
4) The implementation of the data warehouse has not only given PNB better control and
insight into its operations, it’s also given management the perspective it requires to
achieve the bank’s vision of 15 crores customers and business of Rs 10,00,000 crores by
2013.
5) Other benefits are:
• 12 lakh man days saved per year.
• 45,000 leads have been converted into B 1,050 crores of business.
• Provided the support PNB required to focus on customized products and services
to a specific segment of customers.
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CHAPTER 7: FUTURE SCOPE
There are many factors which will continue to influence and shape of the banking industry,
These include data quality, rising storage and network requirements, IT capabilities and business
requirements. Keeping these factors in mind, we suggest use of upcoming trends in business
intelligence which if adopted can bring about a radical change in information management.
1) BI in the Cloud
The data can be transferred to the cloud and once data has been transferred to the Cloud,
there are numerous cost-effective BI and big data tools available for organisations to take
advantage of, along with the obtaining the desired reach.
2) Mobile BI
Mobile business intelligence offers huge advantages for banking organisations,
particularly those with increasingly mobile and remote workforces. It means that staff
and management are never disconnected from the tools that help them make business
decisions.
3) Analytics
It uses algorithms to search for patterns and explanations. It looks at historical data to
predict future activity for better business decision making. Analytics will help companies
differentiate themselves, it will allow them to run more efficiently, make the most of their
customers and increase profitability. Analytics provides organisations with actionable
intelligence. While BI has traditionally been hard to create a business case for, analytics
has a direct correlation to an organisation’s top or bottom line. The three biggest trends
surrounding analytics the industry are: Optimisation—the combination of business rules
for optimised decision management; consumable analytics—the visual presentation of
increasingly complex data; and new data analytics—the analysis of new types of data,
such as social media, location information, etc.
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4) In-memory analytics
In-memory analytics tools—such as Qlikview, Spofire and Tableau—allow for the
querying and analysing of data from a computer’s RAM, resulting in quick and simple
data exploration for BI and analytic applications. Rather than relying on centrally
controlled, monolithic data warehouses, users are able to download large amounts (up to
1 terabyte) of data onto their own computer and explore that information for proving
theories and making business decisions throughout an organisation. Given the speed, ease
and affordability with which these tools can put power back into the hands of the users.
5) The Agile approach to BI
An Agile approach can be used to incrementally remove operational costs and if
deployed, can return great benefits to any organisation. Agile provides a streamlined
framework for building business intelligence/data warehousing (BIDW) applications that
regularly delivers faster results using just a quarter of the developer hours of a traditional
waterfall approach.
6) Anti-Money Laundering Software linked with Data Warehouse
Transaction monitoring systems help fight money laundering by identifying
uncharacteristic deposits or withdrawals, identification of suspicious transactions can
help businesses file Suspicious Activity Reports, or SARs.
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