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• Cognizant 20-20 Insights




The Economic Value of Data
Part 1 in a multipart series
Big data analytics promises to boost customer centricity and
profitability for financial services firms, especially when applied to
market research, customer segmentation, product testing, product
development and customer service.

      Executive Summary                                      type of data in the right format for analysis; too
                                                             much data, as some organizations have learned,
      Big data has gained significant influence in recent
                                                             may not always be beneficial. We also advise
      years and is rapidly transforming the business,
                                                             companies to start small and take manageable
      operations and technology landscape for a
                                                             steps toward incorporating big data analytics into
      myriad of industries. Early adopters — particularly
                                                             their operating models.
      in retail and consumer products — have already
      derived significant business insights from big         This white paper is the first in a series that
      data management best practices, such as analysis       presents our perspective on the economic value
      of both the growing pools of structured trans-         that can be derived from big data analytics by
      actional data from operations systems and the          financial services companies. Subsequent white
      unstructured and semi-structured data generated        papers will cover specific functional areas across
      by social media interactions.                          the financial services spectrum in which big data
                                                             analytics can have a significant impact.
      According to a recent report by International
      Data Corp., big data is the next essential business
                                                             From the Beginning
      capability and a foundation for the intelligent
      economy. According to the report, the worldwide        Coined by McKinsey & Co., the term “big data”2
      big data market is expected to grow from $3.2          describes large datasets that cannot be captured,
      billion in 2010 to $16.9 billion in 2015, a compound   managed or processed by commonly used software
      annual growth rate (CAGR) of 40%.1                     tools within a reasonable amount of time and at a
                                                             reasonable cost. According to IDC, about 90% of
      Investments in big data solutions have helped          available data today has been generated in just the
      enterprises achieve customer centricity and            last two years. In fact, IDC estimates that:
      material gains in pricing and profitability. This
      whitepaper posits potential opportunities that big
                                                             •	 Data volumes are growing at 50% per year, or
                                                               more than doubling every two years.
      data analytics can create for financial services
      companies, citing specific business opportunities      •	 Machine-generated data is projected to rise
      and benefits. Our empirical experience suggests          from today’s 200 exabytes to 1,000 exabytes
      it is critical to generate the right amount and          by 2015.




       cognizant 20-20 insights | february 2013
•	 Multi-structured   data content is the primary       can now be aggregated and analyzed to identify
                      driver of new data. 80% of this new data is          escalation and complaint triggers, understand
                      digital, which is complex to analyze in its native   fraud patterns, manage alerts, reduce credit risk
                      structure.                                           and build social media dashboards. These devel-
                                                                           opments can help financial institutions tailor their
                   •	 Digital
                       data is growing at 62% annually vs.
                                                                           products and build strategy roadmaps aligned
                structured data at 22%.3 This explosion of data
                                                                           with customer expectations. Effective use of
                coupled with the growth in social networking
                                                                           big data will be a key driver for competition in
                and virtualization, has introduced unprece-
                                                                           financial services, and companies that use data
                dented opportunities for companies to better
                                                                           more effectively will secure an edge in the mar-
                                connect with consumers and
                                                                           ketplace.
         Most companies understandwhere the markets
                                as well as
                                             their sentiments,

         have yet to find are heading.                                     Retaining customers and satisfying consumer
                                                                           expectations are among the most serious
    precise answers to                Most companies have yet to           challenges    facing    financial   institutions.
  the challenges posed                find precise answers to the          Sentiment analysis and predictive analysis are
      by fast-changing                challenges posed by fast-
                                      changing consumer demands;
                                                                           two techniques that they can use to effectively
                                                                           address these and other key challenges.
   consumer demands;                  many lack the ability to process
  many lack the ability               data in near real-time or            Capturing Customer Feedback Through
                                                                           Sentiment Analysis
     to process data in               convert interactions into trans-
                                      actions. Big data analytics,         Consumers today are just as willing to share
      near real-time or               therefore, has become one of         their thoughts on social media platforms such
   convert interactions               the most frequently discussed        as Facebook and Twitter as express them to
     into transactions.               topics for many business
                                      leaders.
                                                                           a customer service representative over the
                                                                           phone, Web site or in person. When captured and
                                                                           managed, such information can provide valuable
              Emerging big data tools provide companies with
                                                                           insights into what customers are thinking. In
              the ability to analyze far greater quantities and
                                                                           addition, online customer reviews on Web sites
              types of data in a shorter span of time. It includes
                                                                           such as Amazon and Yelp are fast emerging as
              structured datasets, such as information stored
                                                                           major influencers of vendor/provider selection
                                  in    databases;     semi-struc-
                                                                           and purchasing behavior. This means financial
                                  tured data like XML files and
            Snippets of RSS feeds; and unstructured                        services institutions must carefully review this
                                                                           proliferating stream of unfiltered content to
    unstructured data datasets, such as images,                            gauge customer expectations and opinions on
can be interpreted and videos, text messages, e-mails                      product offerings and then act accordingly.
  analyzed, delivering and documents. New technolo-
                                  gies can help uncover insights           Traditionally, companies have collected consumer
     insights that can hidden within these large                           feedback using survey and focus group results.
      determine likely datasets. While retailers and                       These tools may gauge consumer sentiment, but
   consumer response technology companies such as
                                  Google, Walmart, Amazon and
                                                                           they may not necessarily capture emerging trends
                                                                           or hidden insights, particularly on a real-time
        (both favorable Sears have made significant                        basis. A negative opinion of a bank’s offering can
   and unfavorable) to developments on this front,                         potentially lead to dramatic customer churn. For
       decisions made the doorsthe financial services
                                  open for
                                              are just starting to         example, in September 2011, Bank of America
                                                                           announced its decision to charge customers a
           by the bank. industry, which stands to gain                     monthly debit card fee. Three days later, the bank
                                  significant advances in areas            withdrew the decision after a customer uproar
              such as market research, customer segmenta-                  and threat of attrition. The reversal occurred after
              tion, product testing, product development and               customers petitioned the bank and mobilized to
              customer service.                                            close their accounts and take their banking and
                                                                           investment business elsewhere.4
                   For example, text captured from credit applica-
                   tions, account opening interviews, call center          Sentiment analysis tools aim to capture customer
                   notes, mortgage application notes, social media         feedback from social media platforms and
                   chatter and other customer service interactions         customer service interactions, among other



                                           cognizant 20-20 insights        2
sources, and help banks evaluate the potential                service features. This can help banks generate
impact of such decisions. Sentiment analysis                  customer “wish-lists” and incorporate these into
enables organizations to associate words used                 their product roadmaps.
in unstructured communications and tie them
to consumer emotions and sentiment on a topic.                Sentiment analysis can also help banks reward
These findings can serve as key inputs into                   customers effectively. This is extremely important
strategic decision-making.                                    across the industry because account switching
                                                              costs are relatively low and customer churn is
The idea is to use technology to create codes                 a major challenge. By examining customer con-
that analyze the Web and provide insights into                fidence indices that are
consumer sentiment on a much larger scale and                 driven by specific data
at a much faster rate than the findings revealed              elements (product, func- By examining customer
by surveys or focus groups. Snippets of unstruc-              tionality,  content     and confidence indices that
tured data can be interpreted and analyzed,                   price), banks can judge the
delivering insights that can determine likely                 mood of the market and
                                                                                              are driven by specific
consumer response (both favorable and unfavor-                decide how to best reward data elements (product,
able) to decisions made by the bank.                          their customers. Success- functionality, content
                                                              ful execution drives loyalty
Consider the following customer scenarios and                 and also attracts new cus-
                                                                                              and price), banks can
statements:                                                   tomers. Figure 1 illustrates judge the mood of the
                                                              how banks can effective- market and decide how
•	 “ABC Bank’s small business offering is useful              ly satisfy a disgruntled
  for new businesses and entrepreneurs. The
                                                              customer using the afore-
                                                                                              to best reward their
  lack of a same-day payment facility is a downer,
                                                              mentioned technique.            customers. Successful
  though.”
                                                                                                execution drives loyalty
•	 “The  feature to view both business and                    Although the technologies
  personal accounts is really cool, although they             behind sentiment analysis and also attracts         new
  really need to improve their customer service.”             are still maturing, many of customers.
                                                              the tools and techniques
The sentiment analysis tool would pick up words               are advanced enough for financial services insti-
like “useful,” “lack” and “improve” and attach                tutions to derive incremental value by under-
contextual meaning to generate graphs and                     standing customer likes, dislikes and preferences
reports, which can then be used by the bank                   for product and service improvements. Clearly,
to satisfy customer expectations. Additionally,               early adoptors will gain a competitive advantage
reports can be generated to illustrate trends and             going forward.
opinions on individual product and customer



Converting Detractors into Advocates




           Michael has recently           • The nature of the e-mails          • The bank sends Michael a
           registered several              suggests a disgruntled               personal note addressing his
           complaints with customer        customer that is likely to churn.    concerns.
           care at his bank.               The bank recognizes this and
                                           takes immediate action.
                                                                               • The bank offers to refinance
                                                                                his auto loan at a much better
                                          • The bank knows Michael has          rate, saving him money and
                                           a new car loan.                      gaining his loyalty in return.




Figure 1



                           cognizant 20-20 insights            3
Using Predictive Analytics to                              churn or favorable response to a particular
                  Capitalize on Customer Insights                            marketing campaign. For example, our work with
                                                                             Merchant Rewards International, a provider of
              Customers across the globe are increasingly
                                                                             credit card processing services, indicates a higher
              demanding simple, fast and inexpensive means
                                                                             response rate for offers aligned with previous
              to conduct both financial and purchasing trans-
                                                                             transaction behavior and buying propensity.
              actions. However, consumer needs are becoming
                               more diverse and unpredictable,               Predictive analytics can help banks build models
      Institutions can placing tremendous pressure                           based on customer spending behavior and
   create pre-defined on companies to fulfill them.
                               Ongoing economic challenges,
                                                                             product usage to pinpoint products and services
                                                                             that customers might find more useful and that
     profiles, thereby accelerating globalization and                        financial institutions can deliver more effectively.
revealing a history of provider choice means financial                       Such a model can help banks develop an efficient
 higher fraud volume services firms must meet and
                               exceed traditional expecta-
                                                                             cross-sell offer, helping them increase their share
                                                                             of wallet, garner loyalty and increase profitability.
    through purchase tions. While failing to respond
             types and to dynamically changing expec-                        For example, profiling technology can help credit
          ticket sizes. tations is problematic, a larger
                               challenge is correctly predict-
                                                                             card companies identify transactions, cardhold-
                                                                             ers and merchants that exhibit a high probabil-
                               ing consumer needs and desires                ity of fraud. Institutions can create pre-defined
              and responding, just in time, with the right set of            profiles, thereby revealing a history of higher
              products and services.                                         fraud volume through purchase types and ticket
                                                                             sizes.
                  Predictive analytic techniques can be used to mine
                  large amounts of historical data and determine             Furthermore, predictive analysis can identify
                  the likely occurrence of events in the future.             aberrant behavior patterns and help financial
                  By querying, visualizing and reporting these               institutions prevent fraud. Collecting data from
                  datasets, companies can generate actionable                multiple sources, such as Web behavior and point-
                  insights. Changing data over time can illuminate           of-sale inputs, and correlating it with aggregated
                  behavioral and transactional patterns that can             data compiled from other financial services
                  help with move-forward decisions on product and            firms by third-party providers, can help banks
                  service strategies.                                        and brokers detect fraud earlier than existing
                                                                             approaches. Big data analytics not only helps
                  Regression and response models are among the               financial institutions preserve the long-sought-
                  techniques that financial institutions can use to          after “instant transaction user experience,” but it
                  determine, for example, the likelihood of customer         can also safeguard them against fraud.



                  Unleashing Machine Intelligence




                             Michael uses his credit     • The predictive analytics system       • The system allows the
                             card to perform an online     determines Michael’s location,          transaction to go through
                             transaction.                  time, transaction category and          if the generated score
                                                           merchant.                               probabilistically
                                                                                                   determines that the
                                                         • The system then compares                purchaser is actually
                                                           these details with Michael’s
                                                                                                   Michael.
                                                           past purchase behavior and
                                                           calculates a score.



                  Figure 2



                                          cognizant 20-20 insights           4
A good example is the ongoing refinement of               talent to accelerate their analytics efforts, as they
neural network technology5 to assess whether              often lack internal expertise or cannot afford to
a credit card transaction is being performed              stretch existing resources. In some cases, instead
by the real cardholder or someone committing              of hiring and motivating talent internally, they are
fraud. The transaction is scored against a pre-           engaging third-party providers to supply talent
defined profile, and if the score passes an estab-        on an “as needed” basis.
lished cutoff, it is approved; otherwise, it is held
for a fraud check. Banks have used this type of           Looking Ahead
artificial intelligence technology since the early        Big data analytics can help financial institutions
1990s to perform pattern recognition and spot             derive significant benefits by increasing customer
fraudulent transactions. However, big data tech-          satisfaction, retention and expansion through
nologies make the process faster and more cost-           more effective cross-selling and improvement of
efficient, accurate and robust.                           their fraud and risk management capabilities. The
                                                          economic value of data will be
Financial institutions can also create ‘”predictive       realized only when financial
scorecards,” which can help determine the                 institutions fully endorse As risk managers
likelihood of customers defaulting on payments            big data analytics and invest frequently say,
in the near future. Among the parameters to               in innovation. Although the it is better to be
consider are late utility bill payments, late car         possibilities are endless,
insurance payments, increases in purchases                numerous challenges must approximately right
compared with monthly averages and listening              be addressed before the than precisely wrong.
and learning from relevant social media conver-           benefits can be fully realized.
sations.
                                                          In a special report in The Economist, author
As with sentiment analysis, additional research           Kenneth Cukier reveals that the recent global
and development is required to improve the                financial crisis sheds light on how banks and
accuracy and effectiveness of predictive analytic         ratings agencies relied on models requiring
techniques. However, when deployed strategical-           vast volumes of information but failed to reflect
ly, these tools can help banks gain a significant         financial risk in the real world.6 Therefore, to
advantage in a competitive macro-economic envi-           capitalize on big data analytics’ opportunities and
ronment.                                                  realize significant business value, it is advisable
                                                          for financial institutions to start small and grow
Areas such as delinquency propensity, loss
                                                          gradually. Firms must find the right balance of
mitigation and cross-sell/next-best-offer scripting
                                                          required information and desired insight. As risk
are all specific areas offering a solid business case
                                                          managers frequently say, it is better to be approx-
for use of analytics techniques. Financial institu-
                                                          imately right than precisely wrong.
tions are making investments and hiring outside




Footnotes
1	
     “Worldwide Big Data Technology and Services 2012-2015 Forecast,” IDC, March 7, 2012,
     http://www.idc.com/getdoc.jsp?containerId=prUS23355112#.UQwxhuTAeE4.
2	
     “Big Data: The Next Frontier for Innovation, Competition and Productivity,” McKinsey Global Institute,
     May 2011, http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_
     next_frontier_for_innovation.
3	
     ”Worldwide Big Data Technology and Services Forecast,” IDC.
4	
     Tara Siegel Bernard, “In Retreat, Bank of America Cancels Debit Card Fee,” The New York Times,
     Nov. 1, 2011, http://www.nytimes.com/2011/11/02/business/bank-of-america-drops-plan-for-debit-card-fee.
     html?_r=0.
5	
     Donald F. Specht, “Probabilistic Neural Networks,” ScienceDirect, 1990,
     http://www.sciencedirect.com/science/article/pii/089360809090049Q.
6	
     “Data, Data Everywhere,” The Economist, Feb. 27, 2010, http://www.emc.com/collateral/analyst-reports/
     ar-the-economist-data-data-everywhere.pdf.


                         cognizant 20-20 insights         5
References
•	 “Crunching the Numbers,” The Economist, May 19, 2012, http://www.economist.com/node/21554743,
   http://www.information-management.com/news/predictive-analytics-making-little-decisions-with-
   big-data-10023151-1.html.
•	 Julianna DeLua, “Big Data Meets Sentiment Analysis,” The Informatica Blog, June 27, 2011,
   http://blogs.informatica.com/perspectives/2011/06/27/big-data-meets-sentiment-analysis/.
•	 James Taylor, “Predictive Analytics: Making Little Decisions with Big Data,” Information
   Management, Sept. 12, 2012, http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-
   data-guide-1522052.pdf.
•	 “Financial Services Data Management: Big Data Technology in Financial Services,” Oracle Corp., June
   2012, http://www.oracle.com/us/industries/financial-services/bigdata-in-fs-final-wp-1664665.pdf.
•	 David Wallace, “Big Data Management for Retail Banks,” SAS, The Knowledge Exchange, July 6,
   2012, http://www.sas.com/knowledge-exchange/risk/integrated-risk/big-data-management-for-
   retail-banks/index.html.
•	 Christopher Papagianis, “Can Silicon Valley Fix the Mortgage Market?” Reuters, April 25, 2012,
   http://blogs.reuters.com/christopher-papagianis/tag/big-data/.



About the Authors
Vin Malhotra is a Partner with Cognizant Business Consulting’s Banking and Financial Services Practice.
He has 25-plus years of experience in management consulting, focused on retail, commercial and
mortgage banking clients. His clients have included international and regional banks, credit unions
and Fortune 1000 firms in the BPO, payments and financial technology space. He has served clients in
multiple geographies, with project delivery in the U.S., Latin America, Central America and Europe. Vin
can be reached at Vin.Malhotra@cognizant.com.

Sudhir Jain is a Senior Manager within Cognizant Business Consulting’s Banking and Financial Services
Practice. He has 10-plus years of experience in capital markets, risk management, collateral management
and margining with top-tier banks in the U.S., Singapore and India. Sudhir leads a team of business con-
sultants who provide advisory services and software development to leading banks. He can be reached
at Sudhir.Jain@Cognizant.com.

Rahul Kumar is a Senior Consultant within Cognizant Business Consulting’s Banking and Financial
Services Practice. Rahul has three-plus years of experience in consumer banking at one of the world’s
largest banks. Rahul can be reached at Rahul.Kumar8@cognizant.com.




About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-
sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in
Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry
and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50
delivery centers worldwide and approximately 156,700 employees as of December 31, 2012, Cognizant is a member of
the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing
and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.


                                         World Headquarters                  European Headquarters                 India Operations Headquarters
                                         500 Frank W. Burr Blvd.             1 Kingdom Street                      #5/535, Old Mahabalipuram Road
                                         Teaneck, NJ 07666 USA               Paddington Central                    Okkiyam Pettai, Thoraipakkam
                                         Phone: +1 201 801 0233              London W2 6BD                         Chennai, 600 096 India
                                         Fax: +1 201 801 0243                Phone: +44 (0) 20 7297 7600           Phone: +91 (0) 44 4209 6000
                                         Toll Free: +1 888 937 3277          Fax: +44 (0) 20 7121 0102             Fax: +91 (0) 44 4209 6060
                                         Email: inquiry@cognizant.com        Email: infouk@cognizant.com           Email: inquiryindia@cognizant.com


©
­­ Copyright 2013, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is
subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

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The Economic Value of Data

  • 1. • Cognizant 20-20 Insights The Economic Value of Data Part 1 in a multipart series Big data analytics promises to boost customer centricity and profitability for financial services firms, especially when applied to market research, customer segmentation, product testing, product development and customer service. Executive Summary type of data in the right format for analysis; too much data, as some organizations have learned, Big data has gained significant influence in recent may not always be beneficial. We also advise years and is rapidly transforming the business, companies to start small and take manageable operations and technology landscape for a steps toward incorporating big data analytics into myriad of industries. Early adopters — particularly their operating models. in retail and consumer products — have already derived significant business insights from big This white paper is the first in a series that data management best practices, such as analysis presents our perspective on the economic value of both the growing pools of structured trans- that can be derived from big data analytics by actional data from operations systems and the financial services companies. Subsequent white unstructured and semi-structured data generated papers will cover specific functional areas across by social media interactions. the financial services spectrum in which big data analytics can have a significant impact. According to a recent report by International Data Corp., big data is the next essential business From the Beginning capability and a foundation for the intelligent economy. According to the report, the worldwide Coined by McKinsey & Co., the term “big data”2 big data market is expected to grow from $3.2 describes large datasets that cannot be captured, billion in 2010 to $16.9 billion in 2015, a compound managed or processed by commonly used software annual growth rate (CAGR) of 40%.1 tools within a reasonable amount of time and at a reasonable cost. According to IDC, about 90% of Investments in big data solutions have helped available data today has been generated in just the enterprises achieve customer centricity and last two years. In fact, IDC estimates that: material gains in pricing and profitability. This whitepaper posits potential opportunities that big • Data volumes are growing at 50% per year, or more than doubling every two years. data analytics can create for financial services companies, citing specific business opportunities • Machine-generated data is projected to rise and benefits. Our empirical experience suggests from today’s 200 exabytes to 1,000 exabytes it is critical to generate the right amount and by 2015. cognizant 20-20 insights | february 2013
  • 2. • Multi-structured data content is the primary can now be aggregated and analyzed to identify driver of new data. 80% of this new data is escalation and complaint triggers, understand digital, which is complex to analyze in its native fraud patterns, manage alerts, reduce credit risk structure. and build social media dashboards. These devel- opments can help financial institutions tailor their • Digital data is growing at 62% annually vs. products and build strategy roadmaps aligned structured data at 22%.3 This explosion of data with customer expectations. Effective use of coupled with the growth in social networking big data will be a key driver for competition in and virtualization, has introduced unprece- financial services, and companies that use data dented opportunities for companies to better more effectively will secure an edge in the mar- connect with consumers and ketplace. Most companies understandwhere the markets as well as their sentiments, have yet to find are heading. Retaining customers and satisfying consumer expectations are among the most serious precise answers to Most companies have yet to challenges facing financial institutions. the challenges posed find precise answers to the Sentiment analysis and predictive analysis are by fast-changing challenges posed by fast- changing consumer demands; two techniques that they can use to effectively address these and other key challenges. consumer demands; many lack the ability to process many lack the ability data in near real-time or Capturing Customer Feedback Through Sentiment Analysis to process data in convert interactions into trans- actions. Big data analytics, Consumers today are just as willing to share near real-time or therefore, has become one of their thoughts on social media platforms such convert interactions the most frequently discussed as Facebook and Twitter as express them to into transactions. topics for many business leaders. a customer service representative over the phone, Web site or in person. When captured and managed, such information can provide valuable Emerging big data tools provide companies with insights into what customers are thinking. In the ability to analyze far greater quantities and addition, online customer reviews on Web sites types of data in a shorter span of time. It includes such as Amazon and Yelp are fast emerging as structured datasets, such as information stored major influencers of vendor/provider selection in databases; semi-struc- and purchasing behavior. This means financial tured data like XML files and Snippets of RSS feeds; and unstructured services institutions must carefully review this proliferating stream of unfiltered content to unstructured data datasets, such as images, gauge customer expectations and opinions on can be interpreted and videos, text messages, e-mails product offerings and then act accordingly. analyzed, delivering and documents. New technolo- gies can help uncover insights Traditionally, companies have collected consumer insights that can hidden within these large feedback using survey and focus group results. determine likely datasets. While retailers and These tools may gauge consumer sentiment, but consumer response technology companies such as Google, Walmart, Amazon and they may not necessarily capture emerging trends or hidden insights, particularly on a real-time (both favorable Sears have made significant basis. A negative opinion of a bank’s offering can and unfavorable) to developments on this front, potentially lead to dramatic customer churn. For decisions made the doorsthe financial services open for are just starting to example, in September 2011, Bank of America announced its decision to charge customers a by the bank. industry, which stands to gain monthly debit card fee. Three days later, the bank significant advances in areas withdrew the decision after a customer uproar such as market research, customer segmenta- and threat of attrition. The reversal occurred after tion, product testing, product development and customers petitioned the bank and mobilized to customer service. close their accounts and take their banking and investment business elsewhere.4 For example, text captured from credit applica- tions, account opening interviews, call center Sentiment analysis tools aim to capture customer notes, mortgage application notes, social media feedback from social media platforms and chatter and other customer service interactions customer service interactions, among other cognizant 20-20 insights 2
  • 3. sources, and help banks evaluate the potential service features. This can help banks generate impact of such decisions. Sentiment analysis customer “wish-lists” and incorporate these into enables organizations to associate words used their product roadmaps. in unstructured communications and tie them to consumer emotions and sentiment on a topic. Sentiment analysis can also help banks reward These findings can serve as key inputs into customers effectively. This is extremely important strategic decision-making. across the industry because account switching costs are relatively low and customer churn is The idea is to use technology to create codes a major challenge. By examining customer con- that analyze the Web and provide insights into fidence indices that are consumer sentiment on a much larger scale and driven by specific data at a much faster rate than the findings revealed elements (product, func- By examining customer by surveys or focus groups. Snippets of unstruc- tionality, content and confidence indices that tured data can be interpreted and analyzed, price), banks can judge the delivering insights that can determine likely mood of the market and are driven by specific consumer response (both favorable and unfavor- decide how to best reward data elements (product, able) to decisions made by the bank. their customers. Success- functionality, content ful execution drives loyalty Consider the following customer scenarios and and also attracts new cus- and price), banks can statements: tomers. Figure 1 illustrates judge the mood of the how banks can effective- market and decide how • “ABC Bank’s small business offering is useful ly satisfy a disgruntled for new businesses and entrepreneurs. The customer using the afore- to best reward their lack of a same-day payment facility is a downer, mentioned technique. customers. Successful though.” execution drives loyalty • “The feature to view both business and Although the technologies personal accounts is really cool, although they behind sentiment analysis and also attracts new really need to improve their customer service.” are still maturing, many of customers. the tools and techniques The sentiment analysis tool would pick up words are advanced enough for financial services insti- like “useful,” “lack” and “improve” and attach tutions to derive incremental value by under- contextual meaning to generate graphs and standing customer likes, dislikes and preferences reports, which can then be used by the bank for product and service improvements. Clearly, to satisfy customer expectations. Additionally, early adoptors will gain a competitive advantage reports can be generated to illustrate trends and going forward. opinions on individual product and customer Converting Detractors into Advocates Michael has recently • The nature of the e-mails • The bank sends Michael a registered several suggests a disgruntled personal note addressing his complaints with customer customer that is likely to churn. concerns. care at his bank. The bank recognizes this and takes immediate action. • The bank offers to refinance his auto loan at a much better • The bank knows Michael has rate, saving him money and a new car loan. gaining his loyalty in return. Figure 1 cognizant 20-20 insights 3
  • 4. Using Predictive Analytics to churn or favorable response to a particular Capitalize on Customer Insights marketing campaign. For example, our work with Merchant Rewards International, a provider of Customers across the globe are increasingly credit card processing services, indicates a higher demanding simple, fast and inexpensive means response rate for offers aligned with previous to conduct both financial and purchasing trans- transaction behavior and buying propensity. actions. However, consumer needs are becoming more diverse and unpredictable, Predictive analytics can help banks build models Institutions can placing tremendous pressure based on customer spending behavior and create pre-defined on companies to fulfill them. Ongoing economic challenges, product usage to pinpoint products and services that customers might find more useful and that profiles, thereby accelerating globalization and financial institutions can deliver more effectively. revealing a history of provider choice means financial Such a model can help banks develop an efficient higher fraud volume services firms must meet and exceed traditional expecta- cross-sell offer, helping them increase their share of wallet, garner loyalty and increase profitability. through purchase tions. While failing to respond types and to dynamically changing expec- For example, profiling technology can help credit ticket sizes. tations is problematic, a larger challenge is correctly predict- card companies identify transactions, cardhold- ers and merchants that exhibit a high probabil- ing consumer needs and desires ity of fraud. Institutions can create pre-defined and responding, just in time, with the right set of profiles, thereby revealing a history of higher products and services. fraud volume through purchase types and ticket sizes. Predictive analytic techniques can be used to mine large amounts of historical data and determine Furthermore, predictive analysis can identify the likely occurrence of events in the future. aberrant behavior patterns and help financial By querying, visualizing and reporting these institutions prevent fraud. Collecting data from datasets, companies can generate actionable multiple sources, such as Web behavior and point- insights. Changing data over time can illuminate of-sale inputs, and correlating it with aggregated behavioral and transactional patterns that can data compiled from other financial services help with move-forward decisions on product and firms by third-party providers, can help banks service strategies. and brokers detect fraud earlier than existing approaches. Big data analytics not only helps Regression and response models are among the financial institutions preserve the long-sought- techniques that financial institutions can use to after “instant transaction user experience,” but it determine, for example, the likelihood of customer can also safeguard them against fraud. Unleashing Machine Intelligence Michael uses his credit • The predictive analytics system • The system allows the card to perform an online determines Michael’s location, transaction to go through transaction. time, transaction category and if the generated score merchant. probabilistically determines that the • The system then compares purchaser is actually these details with Michael’s Michael. past purchase behavior and calculates a score. Figure 2 cognizant 20-20 insights 4
  • 5. A good example is the ongoing refinement of talent to accelerate their analytics efforts, as they neural network technology5 to assess whether often lack internal expertise or cannot afford to a credit card transaction is being performed stretch existing resources. In some cases, instead by the real cardholder or someone committing of hiring and motivating talent internally, they are fraud. The transaction is scored against a pre- engaging third-party providers to supply talent defined profile, and if the score passes an estab- on an “as needed” basis. lished cutoff, it is approved; otherwise, it is held for a fraud check. Banks have used this type of Looking Ahead artificial intelligence technology since the early Big data analytics can help financial institutions 1990s to perform pattern recognition and spot derive significant benefits by increasing customer fraudulent transactions. However, big data tech- satisfaction, retention and expansion through nologies make the process faster and more cost- more effective cross-selling and improvement of efficient, accurate and robust. their fraud and risk management capabilities. The economic value of data will be Financial institutions can also create ‘”predictive realized only when financial scorecards,” which can help determine the institutions fully endorse As risk managers likelihood of customers defaulting on payments big data analytics and invest frequently say, in the near future. Among the parameters to in innovation. Although the it is better to be consider are late utility bill payments, late car possibilities are endless, insurance payments, increases in purchases numerous challenges must approximately right compared with monthly averages and listening be addressed before the than precisely wrong. and learning from relevant social media conver- benefits can be fully realized. sations. In a special report in The Economist, author As with sentiment analysis, additional research Kenneth Cukier reveals that the recent global and development is required to improve the financial crisis sheds light on how banks and accuracy and effectiveness of predictive analytic ratings agencies relied on models requiring techniques. However, when deployed strategical- vast volumes of information but failed to reflect ly, these tools can help banks gain a significant financial risk in the real world.6 Therefore, to advantage in a competitive macro-economic envi- capitalize on big data analytics’ opportunities and ronment. realize significant business value, it is advisable for financial institutions to start small and grow Areas such as delinquency propensity, loss gradually. Firms must find the right balance of mitigation and cross-sell/next-best-offer scripting required information and desired insight. As risk are all specific areas offering a solid business case managers frequently say, it is better to be approx- for use of analytics techniques. Financial institu- imately right than precisely wrong. tions are making investments and hiring outside Footnotes 1 “Worldwide Big Data Technology and Services 2012-2015 Forecast,” IDC, March 7, 2012, http://www.idc.com/getdoc.jsp?containerId=prUS23355112#.UQwxhuTAeE4. 2 “Big Data: The Next Frontier for Innovation, Competition and Productivity,” McKinsey Global Institute, May 2011, http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_ next_frontier_for_innovation. 3 ”Worldwide Big Data Technology and Services Forecast,” IDC. 4 Tara Siegel Bernard, “In Retreat, Bank of America Cancels Debit Card Fee,” The New York Times, Nov. 1, 2011, http://www.nytimes.com/2011/11/02/business/bank-of-america-drops-plan-for-debit-card-fee. html?_r=0. 5 Donald F. Specht, “Probabilistic Neural Networks,” ScienceDirect, 1990, http://www.sciencedirect.com/science/article/pii/089360809090049Q. 6 “Data, Data Everywhere,” The Economist, Feb. 27, 2010, http://www.emc.com/collateral/analyst-reports/ ar-the-economist-data-data-everywhere.pdf. cognizant 20-20 insights 5
  • 6. References • “Crunching the Numbers,” The Economist, May 19, 2012, http://www.economist.com/node/21554743, http://www.information-management.com/news/predictive-analytics-making-little-decisions-with- big-data-10023151-1.html. • Julianna DeLua, “Big Data Meets Sentiment Analysis,” The Informatica Blog, June 27, 2011, http://blogs.informatica.com/perspectives/2011/06/27/big-data-meets-sentiment-analysis/. • James Taylor, “Predictive Analytics: Making Little Decisions with Big Data,” Information Management, Sept. 12, 2012, http://www.oracle.com/technetwork/topics/entarch/articles/oea-big- data-guide-1522052.pdf. • “Financial Services Data Management: Big Data Technology in Financial Services,” Oracle Corp., June 2012, http://www.oracle.com/us/industries/financial-services/bigdata-in-fs-final-wp-1664665.pdf. • David Wallace, “Big Data Management for Retail Banks,” SAS, The Knowledge Exchange, July 6, 2012, http://www.sas.com/knowledge-exchange/risk/integrated-risk/big-data-management-for- retail-banks/index.html. • Christopher Papagianis, “Can Silicon Valley Fix the Mortgage Market?” Reuters, April 25, 2012, http://blogs.reuters.com/christopher-papagianis/tag/big-data/. About the Authors Vin Malhotra is a Partner with Cognizant Business Consulting’s Banking and Financial Services Practice. He has 25-plus years of experience in management consulting, focused on retail, commercial and mortgage banking clients. His clients have included international and regional banks, credit unions and Fortune 1000 firms in the BPO, payments and financial technology space. He has served clients in multiple geographies, with project delivery in the U.S., Latin America, Central America and Europe. Vin can be reached at Vin.Malhotra@cognizant.com. Sudhir Jain is a Senior Manager within Cognizant Business Consulting’s Banking and Financial Services Practice. He has 10-plus years of experience in capital markets, risk management, collateral management and margining with top-tier banks in the U.S., Singapore and India. Sudhir leads a team of business con- sultants who provide advisory services and software development to leading banks. He can be reached at Sudhir.Jain@Cognizant.com. Rahul Kumar is a Senior Consultant within Cognizant Business Consulting’s Banking and Financial Services Practice. Rahul has three-plus years of experience in consumer banking at one of the world’s largest banks. Rahul can be reached at Rahul.Kumar8@cognizant.com. About Cognizant Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out- sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 156,700 employees as of December 31, 2012, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com © ­­ Copyright 2013, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.