Wealth Management: How Digital & Learning Algorithms Advance Holistic Advice

Wealth Management:
How Digital & Learning
Algorithms Advance
Holistic Advice
Enhanced digital experiences, more revealing engagement
analytics and deeper forms of artificial intelligence will
deliver a clearer understanding of client context and facilitate
more informed, hyper-personalized financial advice, our latest
research reveals.
September 2018
DIGITAL BUSINESS
COGNIZANT REPORTS

2
EXECUTIVE SUMMARY
The next generation of wealth advisors are optimistic about, not threatened by, the
impact digital client experiences and artificial intelligence (AI) will have on the delivery of
financial advice.
In March 2018, we polled 351 advisors across the U.S. (see Methodology on page 18) on the
state of digital in wealth management. With financial technology transforming the way
people think about and interact with money,1
we sought to understand wealth advisory
organizations’ readiness for these sweeping changes.
Not surprisingly given the topic, over 80% of our respondents were 25 to 45 years of
age, and the majority self-identified as technology early adopters. These Gen X and
Gen Y financial professionals are tech-savvy digital natives, comfortably at home in both
the digital and analog world — and they increasingly blur the distinction between these
channel lines.
When we analyzed the responses from this younger advisor cohort, what drew our attention
were the priorities they highlighted for the delivery of wealth management services, and
the integrated role they envision for humans and technology. (They also shared their views
on process automation, a critical topic but one that falls outside of the parameters of this
report.) They seek the business advantages that arise when technology augments and
extends the human touch.
Factors Shaping The Future of Advice
Our respondents are technology optimists, and they presented a clear point of view and a
specific set of recommendations regarding where wealth management practice will benefit
from enhanced process digitization and machine intelligence:
• They see future customer experience largely shaped by technology. Seventy-one
percent identify as a very high or high priority building digital tools, analytics and
channels to enable and support the experience qualities — available, transparent,
responsive and holistic — demanded by clients.
• They also see (68%) the delivery of holistic advice as being driven by and depen-
dent on the capture of broader sets of data to build more sophisticated analytics
that create opportunities for increased immediacy and relevance. In addition, 78%
2
Digital Business
| Wealth Management: How Digital & Learning Algorithms Advance Holistic Advice

3Wealth Management: How Digital & Learning Algorithms Advance Holistic Advice | 3
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believe that AI can improve the goal planning and portfolio creation processes. They
also see cognitive computing’s potential to advance the goal of the right advice, at the
right time, to the right client, through the right channel.
• They see digital’s promise (AI in particular) as not just an efficiency exercise, but
as a way to drive greater collaboration with clients, colleagues and other pro-
fessionals. A full 80% of our respondents agree that enhanced data and analytics will
drive more personalized advisory services.
• Regarding automated advice, 76% of our respondents believe that automated
advice will grow in capabilities and sophistication and will increasingly find adoption
beyond the mass affluent channel. “Robo” should not be thought of only as a channel
limited to mass affluent clients, but one that will grow in sophistication and application
to core high-net-worth (HNW) clients, and 68% believe that robo as a channel should
be increasingly integrated into the advisor practice as an enabling tool (see Figure 1).
Building a collaboration ecosystem for
advisors, clients and other professionals
is a high priority digital challenge.
So-called “robo-advice” tools will grow
in sophistication and adoption will move
beyond the mass-affluent segment.
The focus of automated advice tools will
expand to AI and cognitive computing.
Automated (”robo”) tools will not be a
separate channel, but will be fully
integrated into the advisor’s practice.
Strongly Agree Agree
39% 32%
42%
40%
41%
34%
28%
27%
Looking Over the Horizon
Respondents were asked which factors will shape the future of advice.
Response base: 295 of 351 total respondents
Source: Cognizant
Figure 1

4 | Wealth Management: How Digital & Learning Algorithms Advance Holistic Advice
Digital Business
The input we received underscores that the realization of these goals is happening
faster than incumbents might believe. We believe the insights received from these early
adopters will help wealth management enterprises to move forward effectively in the ever-
changing digital world and build solutions that complement human advice with AI-enabled
tools that significantly benefit advisors and clients.
Our Recommendations
Early adopters envision a future advice ecosystem consisting of three elements: digitally-
defined customer experiences; the capture, synthesis and analysis of broader data sets
that deepen context; and learning algorithms that deliver meaningful, timely and hyper-
personalized services informed by client needs and behaviors. Based on early adopters’
insights into emerging digital tools and techniques, we recommend wealth-management
enterprises take the following actions:
• KYDC — Know your digital customer. Accelerate the pace for deploying digital expe-
riences. By casting a digital net, we can actively mine the data surfaced from these
journeys with an eye toward understanding customers in a broader context and to the
industry’s goal of delivering holistic advice.
• Integrate systems of record and systems of engagement data to build better client
analytics. These insights can address key relationship goals such as the next best
action and the propensity to buy and/or to leave, as well as enhance the advisor’s abil-
ity to better plan and provide timely, meaningful advice. Seventy-five percent of our
respondents identify harnessing broader data sets to enhance client insights as a key
industry challenge.
• Communicate, collaborate — and nudge.2
Anticipate and build “learning loops” that
begin with an insight, lead to an advisory conversation and recommendation, and then
track behavior. Build these loops in learning algorithms that empower advisors to have
more meaningful client conversations that improve over time.
The future of advice calls for a new approach that is digital-first, AI-first and advisor-first.
This white paper details how wealth management firms can chart a course to relevant,
personalized advisory services.
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5Wealth Management: How Digital & Learning Algorithms Advance Holistic Advice |
Digital Business
MONEY IS ONLY PART OF THE CONVERSATION
Technology is transforming financial services. Digital interactions and AI in the form of machine
learning (ML) algorithms will continue to drive the evolution of wealth management. Our research
suggests this future is increasingly expected, if not demanded, by both investors and advisors. Two-
thirds (66%) of our respondents agree with the proposition that digital technology will fundamentally
change the nature of advice delivery and client interaction.
With younger advisors who identify themselves as early technology adopters forming the majority of
our response base, our findings represent an opportunity to learn more about where and how digital
and AI can add value to their practices. Gen X and Gen Y advisors see digital technology in general,
and AI specifically, as an opportunity to offer more satisfying experiences, improved goal-setting and
portfolio design, and more personalized relationships. This is particularly important when the focus
is on younger investors — 70% of our respondents agree that building relationship models based on
digital interactions will be a critical factor going forward.
We accept that investment products are increasingly commoditized, and wealth management today
pivots around the quality and depth of the relationship between the investor and advisor. That rela-
tionship is increasingly defined by client experience, made more rewarding by digital channels. The
promise of advanced digital, analytics and learning algorithms is to enhance that relationship and
enable advisors to understand the full context of clients and their needs beyond their wealth.
Indeed, the industry is moving in this direction. Financial wellness, life planning and holistic advice all
speak to the desire to understand clients and their wealth in a broader context. Wealth management
is no longer only about investments, but has a broader focus built on understanding investor
behaviors, habits and attitudes. It also pivots on advisors who can coach client behavior to optimize
outcomes. This requires a deeper level of personalization than traditional client analysis provides.
USING SYSTEMS OF ENGAGEMENT TO KNOW YOUR
CUSTOMER BETTER
For wealth management enterprises, the challenge is to build the technology and data ecosystem
to deliver personalization at scale and with speed.
Most data management initiatives within banks and wealth management enterprises have focused
on the tight integration of systems of record data, typically housed in various core transaction,
record-keeping and custodial systems. These efforts, driven primarily by regulatory mandates and
the desire to cross-sell, focus on building a single view of the customer across lines of business that
reflects transactions, investment holdings, and basic, compliance-driven demographic information.
The future of advice calls for a new
approach that is digital-first, AI-first
and advisor-first.

6
This data has traditionally been the primary source of client information and the basis for advisors’
formulation of client investment plans.
Many firms have completed these integration efforts and now seek opportunities to leverage their
systems of record data for more strategic purposes, such as building analytics to respond to or predict
client behavior (e.g., next best action, propensity to buy, predict attrition, etc.). These are ambitious
goals. In our experience, however, it is unproven that systems of record data alone can generate
enough insights to lead wealth management firms to these end points.
A more promising path forward is to enrich this core data set with additional elements sourced
from various systems of engagement (see Figure 2). Examples of these sources include data already
at hand within enterprise systems such as CRM records, service logs and call-center interac-
tions, and data derived from natural language analytics. We can further expand the data pool by
including third-party structured data sources, social media data — even attitudinal, behavioral and
Digital Business
The Marriage of Systems of Record & Systems of Engagement
Incorporating systems of engagement data can help create a richer, more nuanced picture.
Source: Cognizant
Figure 2
• Spending Profile
• Risk Profile
• Professional
Profile
• Social Media
• Insurance/
Health Profile
• CRM Systems
• Call Center
Interactions
• Natural Language
Analytics
• Web/Mobile
Interactions
SYSTEMS OF ENGAGEMENTSYSTEMS OF RECORD
• Accounts
• Transactions
• Holdings
• Duration
• Price/Cost Basis
• Performance
• Owners
• Beneficiaries
• Age
• Address
• Financial Plan
• Stated Goals

observational information. Together, this richer data stew can help advisors to gain deeper customer
context beyond account number, gender, age and address that can inform more meaningful, holistic
wealth conversations.
With the additional insight that systems of engagement data offer, advisors will have the wherewithal
to understand clients beyond wealth tier and life stage — the traditional methods of market segmen-
tation. Wealth advisors will be able to generate forward-looking hypotheses and assess predictive
outcomes. Informed by data beyond systems of record, advisors can connect with clients in a more
timely and personalized way.
Learning Loops: Algorithms that Enable More Personalized Experiences and
Predictive Actions
Learning loops are a “virtuous cycle” that integrates digital experiences, client data from both
systems of record and systems of engagement, advice and then monitoring. The loops (see Figure 3)
provide several key advantages:
• Capture financial and experience data, structured and unstructured, that build deeper
client context.
• Analyze that data via AI and machine learning.
• Derive insights that have predictive or business value.
• Monitor and adjust to suggest future actions to optimize client experience, uptake of advice or
other intended outcomes.
Digital Business
AI/Analytics Opportunities and Feedback/Learning Loops Deliver Deeper
Client Understanding
Empirical: Focused on highly
measurable factors.
Good at linking to firm
economics; weaker at
providing insights.
Contextual: Seeks to intersect
financial needs with life stage
realities.
Better at defining appropriate
financial solutions (e.g.,
retirement Income).
Behavioral: Based on an
understanding of motivations
and needs in context.
Better at defining propensities,
generating insights into
emerging needs, identifying
“moments that matter.”
Traditional Approach:
Assets Under Management
Newer Approach:
Life Stage
Personalized Approach:
Attitudinal
Ultra
High-Net-Worth
High-Net-Worth
Mass
Affluent
Pre-Retiree
Executives
Modern
Families
Widows &
Divorcees
“Unconstrained
Consumers”
“Naïve Risk
Takers”
“Savers”
Source: Cognizant
Figure 3

8
Still firmly at the center are human advisors doing the things people do best, such as reading context,
interpreting behaviors, and making use of intuition and empathy (see Figure 4).
EARLY ADOPTERS’ TAKE: WHERE DIGITAL ADDS PRACTICE VALUE
Our analysis focused on the 84% of respondents who fall in the under 45 age cohort, the majority of
whom self-describe as technology early adopters. We believe this group of advisors will set the road-
map and lead the adoption of digital and AI in their practices.
Early adopters don’t see data and analytics as a threat but as an opportunity and a tool to augment
their practice (see Figure 5, next page). They share a positive vision for technology’s role in providing
more personalized services, and their focus is squarely on the opportunity to gain differentiation.
Thematically, the opportunities they cited fall into two areas: customer acquisition via deeper
analytics and more directed communications, and customer experience via greater self-service and
digital collaboration.
A deeper examination, however, of our data highlights four prominent use cases — client segmen-
tation and needs analysis, client onboarding, goal planning and portfolio creation, and client
communications.
Digital Business
Learning Loops Pull Experience and Data Together
Source: Cognizant
Figure 4
“Nudges”
Integrate Data from
Systems of Engagement
with Systems of Record
Client
Experience
Analytics/Machine
Learning
THE ADVISOR

Digital Business
Data & Analytics Will Drive Personalization
Respondents were asked which unfolding trends will have the greatest impact on the fast-changing
wealth management landscape.
Enriched data and analytics will enable
more personalized advisory services.
AI is the next growth opportunity in
competing for the next generation of
HNW investors.
The key industry challenge is to
broaden data sets that can enhance
insight that drives advice.
42%
38%
41%
38%
38%
34%
Source: Cognizant
Figure 5
Client Acquisition Priorities
Respondents were asked how digital and enhanced analytics will drive client acquisition.
Enhance the quality and speed of
delivering client proposals.
Better identify and engage with
“next-gen” prospects.
Mine prospect and new client data,
both traditional and behavioral, for
better segmentation.
39%41%
42%33%
43%29%
Source: Cognizant
Figure 6
Defining Digital Value: Client Acquisition
We asked our audience which use cases within the customer acquisition process would benefit most
from investments in digital and AI. The opportunities that rose to the top were customization of client
proposals, rethinking the onboarding experience, and leveraging external data collection to build a
more refined and actionable prospect segmentation (see Figure 6).

10
Digital Business
Top Digital Opportunities
Respondents were asked to identify the most promising opportunities for digital to augment advisor
effectiveness.
Client Data Collection
Risk Profiling
Data Aggregation
KYC, Background Checking
Prospecting/Lead Management
68%
36%
35%
34%
18%
Note: Percent citing opportunities to enhance digital delivery above current state.
Source: Cognizant
Figure 7
Defining Digital Value: Client Onboarding
The response we received regarding client onboarding was equally direct. They see opportunities to
improve information-gathering at the onset of the onboarding process to drive self-service via digital
channels during the process, and to streamline onboarding in terms of new account opening, money
movement and transfer of assets (see Figure 8, next page).
Asked where digital solutions would be of most value related to client onboarding, our respondents
led with improving core processes such as the transfer of assets, new account opening and money
movement for initial funding, and addressing client data collection to streamline background checking
(see Figure 9, next page).
The leading specific pain point our respondents cited where digital solutions would be of most value
related to acquisition was improving data collection, followed by improving the processes of client risk
profiling, data aggregation and background checking (see Figure 7).

Digital Business
All Onboard
Respondents were asked to identify the key areas for digital to drive client experience in client
onboarding.
Improve information gathering and
collaboration between advisor and
client during onboarding.
Employ digital tools and AI to help
drive a higher degree of automation
and self-service.
Drive digital to streamline onboarding
tasks: new account opening, money
movement, transfer of assets.
39%41%
42%38%
33%29%
Source: Cognizant
Figure 8
Where Digital Enhances Advisor Effectiveness
effectiveness.
Transfer of Assets
New Account Opening
Money Movement
Client Data Collection
Background/Compliance Validation
62%
61%
60%
58%
56%
Source: Cognizant
Figure 9

12
Digital Business
Personalizing the Investment Process
Respondents were asked to identify key areas for digital and AI to drive better goal planning and
client portfolio outcomes.
Provide greater customization to
client reports.
Perform more accurate risk modeling
using broader analytics and
simulations.
Leverage deeper client-specific insights
to improve goal planning.
42%40%
43%39%
40%38%
Source: Cognizant
Figure 10
Defining Digital Value: Goal Planning & Portfolio Creation
Top opportunities from our research about this topic span using deeper client analytics in the goal
planning process, applying engagement analytics to simulate better risk modeling and delivering
greater customization in client reporting (see Figure 10).
Specific opportunities cited include improving performance monitoring, cash flow projections and
the goal-planning process itself, and translating these goals to manager/investments selection
(see Figure 11, next page).
Defining Digital Value: Client Communications
As a final example, client communications rose to the top as a use case for advisors where a more
sophisticated and integrated use of digital, data and analytics could improve effectiveness. Specifics
highlighted by our respondents include driving personalization to deliver more relevant and
actionable insights (see Figure 12, next page).

Digital Business
Where to Focus: Goal Planning & Portfolio Design
effectiveness.
Monitoring Performance
Against Plan
Providing Cash Flow
Projections
Building a Financial or
Retirement Plan
Manager Selection &
Investment Allocation
Holdings Analysis & Product
Recommendations
59%
57%
57%
57%
53%
Source: Cognizant
Figure 11
Being Digitally Connected
Respondents were asked how digital could best drive client experience around information manage-
ment and communications.
Improve the timeliness and
personalization of client communica-
tions and service alerts.
Drive more relevant and actionable
customer insights from all sources of
client data.
Explore opportunities to use social
media as a communications and
service delivery channel.
43%
41%
40%
38%
36%
33%
Source: Cognizant
Figure 12

QUICK TAKE
Staying Relevant in the
Conversation
Figure 13 depicts an operating model that helps advisers rethink client com-
munications by delivering a heightened digital experience, informed by
enriched data sets and feedback loops. This use case, based on our client
work, consists of a digital-first experience that captures and deploys both
systems of record and systems of engagement data, and keeps the advisor
at the center of the experience.
14
Digital Business
Learning
Loop
ALERT ASSESS ADVISE TRANSACT ADJUST
Systems
of Record
Online research inquiry.
Pre-set news feed.
Communication calendar.
Event trigger.
Enrichments to alerts
profile.
Click rate/downloads from
online research.
Holdings.
Accounts.
Allocations. Products.
Performance & return
profiles.
Product placement. Action vs.
recommendation.
Performance vs. goal.
Risk profile. Specific recommendation.
Jane’s Interests
Pharma
ESG
Biotech
Venture
Alert: an article
of interest …
What is our
current exposure?
What are the
lowest risk
opportunities to
participate?
Time to get
back into
the market/
rebalance?
What are the
areas you
care about
most?
Let’s do further
research…
And update our
interest profile …
Should we adjust
course?
Systems of
Engagement
Source: Cognizant
Figure 13
Use Case: Staying Relevant in the Conversation

Digital Business
THE DELIVERY CHALLENGE FOR WEALTH MANAGEMENT FIRMS
Our research illuminates practical challenges faced when applying digital tools and thinking to the
wealth management function, and poses three “show-me” challenges to enterprises (see Figure 14):
• The first challenge is to fully commit to a digital and AI agenda. This is called out as a primary
challenge by the largest number (43%) of respondents in our survey.
• The second is to keep field utility at the forefront; that is, they want to see their firms test and
roll out solutions that are useful and impact their practices and client conversations.
• The third challenge is timing, especially with respect to working through legacy systems and
processes.
There is a sense of impatience among early adopters that the complexity of addressing legacy infra-
structure, data and systems issues will hold back the digital agenda. This is a practical concern for sure,
but a challenge nonetheless to firms that to stay relevant must take an accelerated, agile approach to
defining their discretionary investments and their product development process.
What Challenges the Success of Your Firm’s Digital Strategy?
Talent
Regulations
Org Resistance
Adoption
Timeline/Legacy
Budget
Leadership
7%
10%
18%
20%
37%
36%
30%
17%
24%
21%
Primary Secondary
2. Roll-out of digital solutions that
demonstrate field value.
43%
31%
1. Skepticism that the organization
is fully embracing digital.
30%
54% 3. Clearing through the thicket of
legacy systems and processes.
Source: Cognizant
Figure 14

LOOKING AHEAD ON THE DIGITAL HIGHWAY
How can wealth management enterprises begin to address the priorities young advisors are asking
for? How do they develop the engaging digital experiences that customers have come to expect? We
recommend the following actions to ensure a higher degree of field success:
• Engage early adopters in the process. The opportunity for enterprise experience design, product
and IT leaders is to harness early adopters’ appetite for hyper-personalized experiences, and to let
them drive the process. Invite them to codevelop requirements, shape the roadmap and be your
beta testers. While Gen X and Y advisors may not yet be managing your largest client books, they
will provide the most productive input about the success or failure of your digital initiatives.
• Get used to “fuzzy data” when integrating data from systems of record and systems of
engagement. We now have the capacity to move beyond short-hand indicators and to integrate
richer data sets containing structured, semi-structured and unstructured information to build pro-
files beyond balances, holdings and transactions. Making sense of these newer data sets requires
that organizations develop capabilities to live with the ever-increasing pools of big data that will
be fuzzy in nature.
While custodial and transactional systems (systems of record) provide organized data, systems
of engagement (sources ranging from CRM to social media and observational input) deliver data
streams that are rich in context and relevance, but less structured. Legacy tools need data in
structured and organized format to generate meaningful insights. AI and machine learning can
enable companies to generate insights from fuzzy, unstructured data sets generated by learning
algorithms, thus filling in contextual and relevance gaps. The insights generated by these systems
of intelligence will support “segment of one” client interactions and will enable wealth manage-
ment to deliver more intimate, hyper-personalized services at scale and speed.
16
Digital Business
While Gen X and Y advisors may
not yet be managing your largest
client books, they will provide the
most productive input about the
success or failure of your digital
initiatives.

Digital Business
• Look for the nudge. Anticipate and build learning loops that begin with an insight, lead to an
advisory conversation and track behavior toward improving the effectiveness of recommenda-
tions. Enhanced analytics should empower advisors to have more meaningful client conversations
that improve over time. Digital channels are critical enablers for capturing richer engagement
data and orchestrating conversations at those most critical a-ha moments. Data at the point of
engagement/interaction helps to inform a series of events that can generate contextual client
communication.
Stream-based processing that is supported by AI/ML models can help advisors to nudge clients
toward financial and life goals with the right advice delivered at the point of interaction. This collab-
oration reinforces the notion that systems are taking care of client needs in the background, and an
always-on ecosystem helps promote client interactions that are far more timely than the usual
periodic check-ins.
In sum, the approach suggested here is not just “digital first” and “AI first” — it is also “advisor first.”
We believe these technologies and tools are moving quickly from emerging to core, but in a manner
that will augment the abilities of the human advisor, not replace her. When well designed and imple-
mented (by heeding the advice of early adopters), digital, AI and machine learning will enhance the
ability of advisors to communicate and collaborate in ways that are more efficient, effective and
meaningful to their clients. This is the future of advice.
Truly getting the customer right will set a clear
direction that executives can use to select the
technologies that are most relevant to fulfill
customer needs. This in turn will help them get
the journey right.

18
Digital Business
In March 2018, Cognizant surveyed 351 professionals to better understand the digital-readiness levels
of wealth advisory organizations. The firms they work for are evenly divided among insurance, bank-
ing and wirehouses. The majority (84%) of respondents are between the ages of 25 and 45. Forty
percent have been in their current roles four to six years.
1 Cognizant, “How Financial Institutions Can Capitalize on the Emotions of Money,” January 2018, www.cognizant.com/white-
papers/how-financial-institutions-can-capitalize-on-the-emotions-of-money-codex2691.pdf.
2 Richard H. Thaler and Cass R. Sunstein, Nudge — Improving Decisions About Health, Wealth, and Happiness, Penguin Books,
2009, www.penguinrandomhouse.com/books/304634/nudge-by-richard-h-thaler-and-cass-r-sunstein/9780143115267/
METHODOLOGY
ENDNOTES
Research Respondents Represent the Future of Wealth Management
FIRM TYPE AGE TECHNOLOGY
50%
54%
34%
38%
26%
4%
7%
26%23%
11%
25%
Wirehouse
Independent Broker-Dealer
Bank Broker-Dealer
Insurance Broker-Dealer
Early Adopter
Follower
Skeptic
25–35
36–45
46–55
>55
Source: Cognizant.
Note: Percentages do not add up to 100% due to rounding.
Figure 15

Digital Business
The author would like to thank Anil Lakhanpal, an AVP in Cognizant Consulting’s Banking & Financial Ser-
vices Practice, and Ed Merchant, a Cognizant Digital Business Vice President and Senior Digital Partner,
for their contributions to this white paper. Also contributing to this white paper were Anand Chan-
dramouli, Director of Research in Cognizant Digital Operations, who helped to instrument the study and
analyze the results, as well as Derek Zhang, from Cognizant’s Wealth & Asset Management Practice,
who provided additional analyticial insights.
Michael Perera
Senior Digital Partner,
Cognizant Consulting’s Wealth
& Asset Management Practice
Michael Perera is Senior Digital Partner and Wealth & Asset
Management Consulting Practice Leader at Cognizant. In this
role, he helps clients define and implement digital strategy, imag-
ine new customer experiences, and transform business models to
optimize product and service delivery. Michael brings over 20 years
of experience in consulting and functional roles at major U.S. firms
such as Fidelity Investments and State Street Corp., where he held
leadership positions in strategy, corporate development, chan-
nel management and new product/platform development across
the advisor, broker-dealer, high-net-worth and asset management
sectors. He holds a master’s degree in business administration
from the Amos Tuck School at Dartmouth College and a bachelor
of arts in economics from the University of Pennsylvania. He can be
reached at Michael.Perera@cognizant.com | www.linkedin.com/in/
michael-perera-9b578b/.
ABOUT THE AUTHOR
ACKNOWLEDGMENTS

ABOUT COGNIZANT
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Okkiyam Pettai, Thoraipakkam
Chennai, 600 096 India
Phone: +91 (0) 44 4209 6000
Fax: +91 (0) 44 4209 6060
European Headquarters
1 Kingdom Street
Paddington Central
London W2 6BD England
Phone: +44 (0) 20 7297 7600
Fax: +44 (0) 20 7121 0102

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