Business Intelligence at Punjab National Bank

GROUP 1Manish Arora 201071Neharika Mallick 201086Puneet Arora 201111Raashi Sodhi 201112

Business Intelligence at Punjab National Bank

A Business Intelligence Project

Business Intelligence at Punjab National Bank

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


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insight into its operations; it’s also given management the perspective it requires to achieve the

bank’s vision.

TABLE OF CONTENTS

EXECUTIVE SUMMARY.............................................................................................................2

TABLE OF FIGURES...................................................................................................................4

CHAPTER1: BANKING INDUSTRY: INTRODUCTION...................................................................51.1 Structure Of Indian Banking Industry..........................................................................................51.2 Challenges Faced By Indian Banking Industry..............................................................................61.3 IT In Banking Sector....................................................................................................................71.4 Data Warehousing In Banking Sector..........................................................................................8

CHAPTER 2: PUNJAB NATIONAL BANK: COMPANY PROFILE...................................................11

CHAPTER 3: PNB: THE BEGINNING OF IT STRATEGY................................................................133.1 SWOT Analysis..........................................................................................................................133.2 IT Strategy................................................................................................................................14

3.2.1 Short Term Goal........................................................................................................................143.2.2 Hardware and Training.............................................................................................................143.2.3 Long-term strategy...................................................................................................................15

CHAPTER 4: CORE BANKING ARCHITECTURE..........................................................................154.1 Culture and technology issues...................................................................................................164.2 Systems....................................................................................................................................164.3 Network design........................................................................................................................164.4 Storage systems........................................................................................................................174.5 Initiatives..................................................................................................................................17

CHAPTER 5: ENTERPRISE WIDE DATA WAREHOUSE: PLANNING.............................................185.1 Requirements...........................................................................................................................195.2 Reasons for choosing EDW........................................................................................................205.3 Challenges during Implementation Phase.................................................................................215.4 Solution Provided for various Business needs...........................................................................235.4.1 MIS and Analytics:.................................................................................................................235.4.2 Customer Relationship Management:....................................................................................235.4.3 Risk Management:.................................................................................................................24

CHAPTER 6: ENTERPRISE DATA WAREHOUSE SOFTWARE.......................................................256.1 Scope........................................................................................................................................256.2 Benefits....................................................................................................................................26


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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..............................................................................................4

Figure 1.2: Banking industry performance......................................................................................5

Figure 1.3: Major banking products and vendors............................................................................6

Figure 1.4: Data Warehouse structure.............................................................................................8

Figure 3.1: SWOT Analysis..........................................................................................................12

Figure 5.1: Project Specs...............................................................................................................20


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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

banks and 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 been included 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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.3 IT In Banking SectorInformation 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


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Figure 1.3: Major banking products and vendors

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


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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 are Exadata (Oracle), TwinFin (Netezza/IBM),

DB2 (IBM), SQM (Microsoft) etc.


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HAPTER 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

Current Account

Fixed Deposit Schemes

AUTO RENEWAL

Credit Schemes

Capital Gain Account Scheme-1988

Doorstep Banking Services

Cards

Nomination Facilities

Deceased claim cases

Centralised Banking Solution

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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HAPTER 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 small software 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

STRENGTHS1) The bank personnel would be able to readily embrace the use of IT.2) An existing pool of qualified knowledge-based personnel would contribute largely to the IT initiatives.3) The financial position of the bank was very sound. There would not be any constraint of funds to facilitate IT initiatives.4) The bank wasn't bound to too much legacy systems and equipment.

WEAKNESSES1) Different Unix OS flavors in different branches.2) Different standalone financial applications on PCs at different branches.3) Lack of interoperability due to disparity in systems.4) Limited expertise on the software packages currently deployed. This increased dependence on vendors.5) Systems audits were pending.6) Most branches did not have a proper LAN in place.7) There was almost no WAN connectivity.

OPPORTUNITIES1) More control through Dashboard for Senior Management covering all KPIs related to Deposits, Advances, Profits, NPAs, etc2) Data Mining Infrastructure Capabilities for mathematical and statistical modelling to determine and predict correlation, patterns, and trends among a variety of measures.

3)Compete more effectively with Private players through Customer Analytics covering Customer

Profiling, Customer Segmentation, Lead Analysis & Cross Sell Analysis

THREATS

1) Lack of continuous Support from Management2) Lack of consistent data for implementing the project3) Lack of support from Managers to go online and use of new technology

SWOT


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Figure 3.1: SWOT Analysis

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.

3.2.1 Short 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.

3.2.2 Hardware 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.

3.2.3 Long-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.

PNB selected 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

migration of the data into Finnacle. By mid May 2002, all data from other software was

successfully migrated into Finnacle.


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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.

4.3 Network 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

allowed PNB to standardize the applications and software needed to provide the banking

services.


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.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

DATA WAREHOUSE:

PLANNING

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.

“Operational efficiency has been one of the key

benefits of this implementation.”

The project has plugged revenue leaks in PNB’s

system which Misra conservatively estimates

in the range of Rs 10 Crore.


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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.

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


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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.

Project Specs Deployment Location: New Delhi 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


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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

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+


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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.

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.


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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

5.4.1 MIS 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

Customer Analytics covering Customer Profiling, Customer Segmentation, Lead

Analysis & Cross Sell Analysis


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.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.

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


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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

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.


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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.

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


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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 Infosphere DataStage

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.

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.


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1) BI in the CloudThe 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.

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


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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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REFERENCES

https://www.pnbindia.in/new/Upload/English/Financials/PDFs/Microsoft%20Word%20-

%20Draft%20Press%20Release%20Q4-%202011-12%20_2_.pdf

http://articles.economictimes.indiatimes.com/2012-07-27/news/32889510_1_net-profit-

pnb-q1-net-npa

http://articles.economictimes.indiatimes.com/2011-01-30/news/28425595_1_deposit-

rates-dana-bank-credit-growth

https://www.pnbindia.in/En/ui/Profile.aspx

http://www.thoughtwareworldwide.com/downloads/BoI_F.pdf

http://www.tomsitpro.com/articles/data_warehouse-business_intelligence-ibm_netezza-

oracle_exadata-twinfin,2-249.html

http://www.rbi.org.in/SCRIPTS/PublicationReportDetails.aspx?UrlPage=&ID=27

http://ijcta.com/documents/volumes/vol2issue4/ijcta2011020425.pdf

http://www.ijcst.com/vol22/1/vivek.pdf

http://www.isrj.net/Sep/2011/Sep/Sawanth.pdf

http://www.dnb.co.in/bfsisectorinindia/BankC6.asp

http://cscjournals.org/csc/manuscript/Journals/IJBRM/volume3/Issue1/IJBRM-64.pdf

http://stockshastra.moneyworks4me.com/learn/indian-banking-industry-future-prospects-

and-sector-overview/

http://en.wikipedia.org/wiki/Banking_in_India

http://www.cio.in/case-study/pnb-deploys-enterprise-wide-data-warehouse

http://202.138.100.134/cio100-2011/ajay-misra-chief-information-officer-punjab-

national-bank

http://pcquest.ciol.com/content/implementation2010/2010/110070118.asp

http://pcquest.ciol.com/content/implementation2010/2010/110060104.asp

http://www.networkmagazineindia.com/200305/tech4.shtml


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Business Intelligence at Punjab National Bank

Business

good brand

facilitate

bulk load

business intelligence

business intelligence

customer relationship

regional data

core banking