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The Economic Value of DataPart 1 in a multipart seriesBig 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 SummaryBig data has gained significant influence in recent years and is rapidly transforming the business, operations and technology landscape for a myriad of industries. Early adopters — particularly in retail and consumer products — have already derived significant business insights from big data management best practices, such as analysis of both the growing pools of structured trans-actional data from operations systems and the unstructured and semi-structured data generated by social media interactions.
According to a recent report by International Data Corp., big data is the next essential business capability and a foundation for the intelligent economy. According to the report, the worldwide big data market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015, a compound annual growth rate (CAGR) of 40%.1
Investments in big data solutions have helped enterprises achieve customer centricity and material gains in pricing and profitability. This whitepaper posits potential opportunities that big data analytics can create for financial services companies, citing specific business opportunities and benefits. Our empirical experience suggests it is critical to generate the right amount and
type of data in the right format for analysis; too much data, as some organizations have learned, may not always be beneficial. We also advise companies to start small and take manageable steps toward incorporating big data analytics into their operating models.
This white paper is the first in a series that presents our perspective on the economic value that can be derived from big data analytics by financial services companies. Subsequent white papers will cover specific functional areas across the financial services spectrum in which big data analytics can have a significant impact.
From the Beginning Coined by McKinsey & Co., the term “big data”2
describes large datasets that cannot be captured, managed or processed by commonly used software tools within a reasonable amount of time and at a reasonable cost. According to IDC, about 90% of available data today has been generated in just the last two years. In fact, IDC estimates that:
• Data volumes are growing at 50% per year, or more than doubling every two years.
• Machine-generated data is projected to rise from today’s 200 exabytes to 1,000 exabytes by 2015.
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• Multi-structured data content is the primary driver of new data. 80% of this new data is digital, which is complex to analyze in its native structure.
• Digital data is growing at 62% annually vs. structured data at 22%.3 This explosion of data coupled with the growth in social networking and virtualization, has introduced unprece-dented opportunities for companies to better
connect with consumers and understand their sentiments, as well as where the markets are heading.
Most companies have yet to find precise answers to the challenges posed by fast-changing consumer demands; many lack the ability to process data in near real-time or convert interactions into trans-actions. Big data analytics, therefore, has become one of the most frequently discussed topics for many business leaders.
Emerging big data tools provide companies with the ability to analyze far greater quantities and types of data in a shorter span of time. It includes structured datasets, such as information stored
in databases; semi-struc-tured data like XML files and RSS feeds; and unstructured datasets, such as images, videos, text messages, e-mails and documents. New technolo-gies can help uncover insights hidden within these large datasets. While retailers and technology companies such as Google, Walmart, Amazon and Sears have made significant developments on this front, the doors are just starting to open for the financial services industry, which stands to gain significant advances in areas
such as market research, customer segmenta-tion, product testing, product development and customer service.
For example, text captured from credit applica-tions, account opening interviews, call center notes, mortgage application notes, social media chatter and other customer service interactions
can now be aggregated and analyzed to identify escalation and complaint triggers, understand fraud patterns, manage alerts, reduce credit risk and build social media dashboards. These devel-opments can help financial institutions tailor their products and build strategy roadmaps aligned with customer expectations. Effective use of big data will be a key driver for competition in financial services, and companies that use data more effectively will secure an edge in the mar-ketplace.
Retaining customers and satisfying consumer expectations are among the most serious challenges facing financial institutions. Sentiment analysis and predictive analysis are two techniques that they can use to effectively address these and other key challenges.
Capturing Customer Feedback Through Sentiment AnalysisConsumers today are just as willing to share their thoughts on social media platforms such as Facebook and Twitter as express them to a customer service representative over the phone, Web site or in person. When captured and managed, such information can provide valuable insights into what customers are thinking. In addition, online customer reviews on Web sites such as Amazon and Yelp are fast emerging as major influencers of vendor/provider selection and purchasing behavior. This means financial services institutions must carefully review this proliferating stream of unfiltered content to gauge customer expectations and opinions on product offerings and then act accordingly.
Traditionally, companies have collected consumer feedback using survey and focus group results. These tools may gauge consumer sentiment, but they may not necessarily capture emerging trends or hidden insights, particularly on a real-time basis. A negative opinion of a bank’s offering can potentially lead to dramatic customer churn. For example, in September 2011, Bank of America announced its decision to charge customers a monthly debit card fee. Three days later, the bank withdrew the decision after a customer uproar and threat of attrition. The reversal occurred after customers petitioned the bank and mobilized to close their accounts and take their banking and investment business elsewhere.4
Sentiment analysis tools aim to capture customer feedback from social media platforms and customer service interactions, among other
Most companies have yet to find
precise answers to the challenges posed
by fast-changing consumer demands;
many lack the ability to process data in near real-time or
convert interactions into transactions.
Snippets of unstructured data
can be interpreted and analyzed, delivering
insights that can determine likely
consumer response (both favorable
and unfavorable) to decisions made
by the bank.
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sources, and help banks evaluate the potential impact of such decisions. Sentiment analysis enables organizations to associate words used in unstructured communications and tie them to consumer emotions and sentiment on a topic. These findings can serve as key inputs into strategic decision-making.
The idea is to use technology to create codes that analyze the Web and provide insights into consumer sentiment on a much larger scale and at a much faster rate than the findings revealed by surveys or focus groups. Snippets of unstruc-tured data can be interpreted and analyzed, delivering insights that can determine likely consumer response (both favorable and unfavor-able) to decisions made by the bank.
Consider the following customer scenarios and statements:
• “ABC Bank’s small business offering is useful for new businesses and entrepreneurs. The lack of a same-day payment facility is a downer, though.”
• “The feature to view both business and personal accounts is really cool, although they really need to improve their customer service.”
The sentiment analysis tool would pick up words like “useful,” “lack” and “improve” and attach contextual meaning to generate graphs and reports, which can then be used by the bank to satisfy customer expectations. Additionally, reports can be generated to illustrate trends and opinions on individual product and customer
service features. This can help banks generate customer “wish-lists” and incorporate these into their product roadmaps.
Sentiment analysis can also help banks reward customers effectively. This is extremely important across the industry because account switching costs are relatively low and customer churn is a major challenge. By examining customer con-fidence indices that are driven by specific data elements (product, func-tionality, content and price), banks can judge the mood of the market and decide how to best reward their customers. Success-ful execution drives loyalty and also attracts new cus-tomers. Figure 1 illustrates how banks can effective-ly satisfy a disgruntled customer using the afore-mentioned technique.
Although the technologies behind sentiment analysis are still maturing, many of the tools and techniques are advanced enough for financial services insti-tutions to derive incremental value by under-standing customer likes, dislikes and preferences for product and service improvements. Clearly, early adoptors will gain a competitive advantage going forward.
Michael has recently registered several complaints with customer care at his bank.
• The nature of the e-mails suggests a disgruntled customer that is likely to churn. The bank recognizes this and takes immediate action.
• The bank knows Michael has a new car loan.
• The bank sends Michael a personal note addressing his concerns.
• The bank offers to refinance his auto loan at a much better rate, saving him money and gaining his loyalty in return.
Figure 1
Converting Detractors into Advocates
By examining customer confidence indices that are driven by specific data elements (product, functionality, content and price), banks can judge the mood of the market and decide how to best reward their customers. Successful execution drives loyalty and also attracts new customers.
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Using Predictive Analytics to Capitalize on Customer InsightsCustomers across the globe are increasingly demanding simple, fast and inexpensive means to conduct both financial and purchasing trans-actions. However, consumer needs are becoming
more diverse and unpredictable, placing tremendous pressure on companies to fulfill them. Ongoing economic challenges, accelerating globalization and provider choice means financial services firms must meet and exceed traditional expecta-tions. While failing to respond to dynamically changing expec-tations is problematic, a larger challenge is correctly predict-ing consumer needs and desires
and responding, just in time, with the right set of products and services.
Predictive analytic techniques can be used to mine large amounts of historical data and determine the likely occurrence of events in the future. By querying, visualizing and reporting these datasets, companies can generate actionable insights. Changing data over time can illuminate behavioral and transactional patterns that can help with move-forward decisions on product and service strategies.
Regression and response models are among the techniques that financial institutions can use to determine, for example, the likelihood of customer
churn or favorable response to a particular marketing campaign. For example, our work with Merchant Rewards International, a provider of credit card processing services, indicates a higher response rate for offers aligned with previous transaction behavior and buying propensity.
Predictive analytics can help banks build models based on customer spending behavior and product usage to pinpoint products and services that customers might find more useful and that financial institutions can deliver more effectively. Such a model can help banks develop an efficient cross-sell offer, helping them increase their share of wallet, garner loyalty and increase profitability.
For example, profiling technology can help credit card companies identify transactions, cardhold-ers and merchants that exhibit a high probabil-ity of fraud. Institutions can create pre-defined profiles, thereby revealing a history of higher fraud volume through purchase types and ticket sizes.
Furthermore, predictive analysis can identify aberrant behavior patterns and help financial institutions prevent fraud. Collecting data from multiple sources, such as Web behavior and point-of-sale inputs, and correlating it with aggregated data compiled from other financial services firms by third-party providers, can help banks and brokers detect fraud earlier than existing approaches. Big data analytics not only helps financial institutions preserve the long-sought-after “instant transaction user experience,” but it can also safeguard them against fraud.
Michael uses his credit card to perform an online transaction.
• The predictive analytics system determines Michael’s location, time, transaction category and merchant.
• The system then compares these details with Michael’s past purchase behavior and calculates a score.
• The system allows the transaction to go through if the generated score probabilistically determines that the purchaser is actually Michael.
Figure 2
Unleashing Machine Intelligence
Institutions can create pre-defined
profiles, thereby revealing a history of higher fraud volume
through purchase types and
ticket sizes.
A good example is the ongoing refinement of neural network technology5 to assess whether a credit card transaction is being performed by the real cardholder or someone committing fraud. The transaction is scored against a pre-defined profile, and if the score passes an estab-lished cutoff, it is approved; otherwise, it is held for a fraud check. Banks have used this type of artificial intelligence technology since the early 1990s to perform pattern recognition and spot fraudulent transactions. However, big data tech-nologies make the process faster and more cost-efficient, accurate and robust.
Financial institutions can also create ‘”predictive scorecards,” which can help determine the likelihood of customers defaulting on payments in the near future. Among the parameters to consider are late utility bill payments, late car insurance payments, increases in purchases compared with monthly averages and listening and learning from relevant social media conver-sations.
As with sentiment analysis, additional research and development is required to improve the accuracy and effectiveness of predictive analytic techniques. However, when deployed strategical-ly, these tools can help banks gain a significant advantage in a competitive macro-economic envi-ronment.
Areas such as delinquency propensity, loss mitigation and cross-sell/next-best-offer scripting are all specific areas offering a solid business case for use of analytics techniques. Financial institu-tions are making investments and hiring outside
talent to accelerate their analytics efforts, as they often lack internal expertise or cannot afford to stretch existing resources. In some cases, instead of hiring and motivating talent internally, they are engaging third-party providers to supply talent on an “as needed” basis.
Looking Ahead Big data analytics can help financial institutions derive significant benefits by increasing customer satisfaction, retention and expansion through more effective cross-selling and improvement of their fraud and risk management capabilities. The economic value of data will be realized only when financial institutions fully endorse big data analytics and invest in innovation. Although the possibilities are endless, numerous challenges must be addressed before the benefits can be fully realized.
In a special report in The Economist, author Kenneth Cukier reveals that the recent global financial crisis sheds light on how banks and ratings agencies relied on models requiring vast volumes of information but failed to reflect financial risk in the real world.6 Therefore, to capitalize on big data analytics’ opportunities and realize significant business value, it is advisable for financial institutions to start small and grow gradually. Firms must find the right balance of required information and desired insight. As risk managers frequently say, it is better to be approx-imately right than precisely wrong.
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As risk managers frequently say, it is better to be approximately right than precisely wrong.
Footnotes1 “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.
About CognizantCognizant (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.
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About the AuthorsVin 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 [email protected].
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 [email protected].
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 [email protected].
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/.