Banking on Big Data: Emerging Returns in Financial Services from Leveraging Large Volumes of Digital Data

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1 Banking on Big Data: Emerging Returns in Financial Services from Leveraging Large Volumes of Digital Data An extract from A TCS 2013 Global Trend Study from Leveraging Large Volumes of Digital Data 1

2 2 from Leveraging Large Volumes of Digital Data

3 In late 2012, Tata Consultancy Services conducted an extensive study on Big Data initiatives in banking and financial services and 11 other industries. Our goal was to understand how banks and other sectors are using tremendous volumes, varieties and velocities of digital data. We focused on understanding where and how Big Data is being used (for example, marketing, sales, customer service, and so on), the extent of investments, expected returns and the key challenges in obtaining such returns, among other issues. The highlights: Banks are spending heavily on Big Data. Median 2012 spending per bank at $19 million was nearly twice the median spending across the 12 industries surveyed. Unstructured and semi-structured data represents a major portion of banks digital data: 45% (vs. 55% of structured data) Banks spent about half (47%) their Big Data investments in 2012 on functions closest to revenue sources: sales, marketing and customer service. Banking finance and accounting officers expect Big Data s greatest potential to be in measuring risk. Marketing managers believe Big Data s greatest returns are in monitoring customer and market perceptions, and comparing prices with competitors while sales managers believe that Big Data will deliver value by helping to determine which customers to focus on and the ones to avoid. Banks expected the average return on their Big Data investments to be sizable in 2012: 44% ROI. A majority of banks are centralizing their Big Data activities, both the processing and analysis of their digital data, either in the IT function or in a central analytics function. The three greatest challenges to banks and financial services companies in generating value from Big Data are: getting business units to share information; determining what types of data to use; and handling the large volume, velocity and variety of data. Banks must overcome the silo issue if they want to tap their data to the fullest extent. Selecting the right data (a mix of internal and external, and both structured and unstructured) is difficult as well particularly banks seek insights on social media data coming from public social networks (with is external and unstructured data of a kind they never had before). And although the banking industry has traditionally been one of the largest users of digital data, Big Data appears to present data management challenges to the industry of a higher scale of complexity. Centralizing Big Data professionals, identifying a leader who can access the highest levels of the organization, and building a broad and deep understanding of all the digital data that is currently available and soon to come on-stream are key steps for banks to take. This would provide greater customer value, increase revenue and profitability, and help in gaining competitive advantage. from Leveraging Large Volumes of Digital Data 3

4 Banks are spending heavily on Big Data, and plan to spend even more Median spending per bank on Big Data in 2012 was $19.3 million, nearly double the median of $10 million across the 12 industries surveyed. The banking and financial services industry is ranked third behind telecommunications and travel/hospitality/ airlines industries in Big Data spending. Companies in the telecommunications and travel/hospitality industries spent a median of $25 million per company on Big Data in Banking respondents predicted that their Big Data spending would grow 46% by 2015, to a median of $28.1 million per company. That was far above the median of $17.5 million projected across all industries. At the biggest banks, each of which can spend $7 billion to $10 billion annually on information technology, that kind of spending would not be unusual. 1 As JPMorgan Chase s chief information officer, Guy Chiarello, told The Wall Street Journal: Big Data is really the theme for Based on our experience and interactions with banking and financial services firms, we see the adoption of Big Data technologies in retail banks and financial services firms - These firms are investing and experimenting more heavily in Big Data and this trend can be attributed to the following reasons: Volume of customers: Both types of financial institutions have a much larger customer base (in some cases, in millions, as for a big retail bank) than do commercial or investment banks. This increases the amount of customer data they need to process and analyze. Large and growing volume of unstructured data: Retail banks need to handle huge volumes of unstructured data. In our experience, the biggest sources of unstructured data within banks today are click streams (data on the bank web pages that customers are perusing) and call center interactions. Furthermore, retail banks are gradually using unstructured data from social media sites to better understand their customers and build a true 360-degree view of the customer. Declining cost of Big Data: There are new technologies now available that allow us to leverage that data, said John Ahrendt, SVP, Enterprise Data and Analytics, Wells Fargo & Co., adding, The price point for working with large sets of data has come down substantially. 3 Unstructured data is a large part of the Big Data mix and expected to grow We asked our banking survey respondents to estimate the percentage of structured data in their Big Data initiatives as well as the proportion of semi-structured and unstructured data. On average, 55% of the data was structured; 26% was unstructured and 20% was semi-structured. (See Exhibit I.) This is not surprising as Bank of America s one-time managing director for Big Data and analytics estimated that 95% of the bank s data was unstructured. 4 4 from Leveraging Large Volumes of Digital Data

5 Exhibit I: Banks Use More Structured Data in Big Data Initiatives Percentage of Structured, Unstructured and Semi-Structured Data in Banking/Financial Services Company Big Data Initiatives 19.67% 54.54% 25.79% Semi-Structured Data Unstructured Data Structured Data Banks use a variety of unstructured and semi-structured data: call center transactions (much of which is voice converted to text), website click streams (for example, to determine why customers get stuck on certain web pages), log data (which tracks how employees use a company s information systems), web analytics and more. Much of this unstructured data comes from internal sources a bank s own systems which make up 70% of the digital data at the banks that we surveyed. (See Exhibit II.) A small (30%) but growing proportion of unstructured data is coming from outside the bank: from social networking sites such as Facebook and Twitter, which banks do not control. from Leveraging Large Volumes of Digital Data 5

6 Exhibit II: Most Data is From Internal Sources Percentage of Banking/Financial Services Company Data from Internal and External Sources 30.39% 69.61% External Data Internal Data By combining structured, unstructured, internal and external data, banks can get a much truer sense of their customers, a 360-degree view. Customer feedback can shift from being random (for example, gathered through episodic market research) to continuous. That, in turn, can help banks precisely understand customer needs for new financial products, reduce customer churn, and more effectively target the millions of dollars they spend on marketing campaigns. 6 from Leveraging Large Volumes of Digital Data

7 Where banks are focusing their Big Data investments Answers to three survey questions shed light on where and why banks and other financial services firms focused their Big Data investments in The first question asked survey respondents with a broad view of how their companies were using Big Data managers in IT, and Big Data and analytics groups to rate their organization s goals for the technology. (See Exhibit III.) Two of the top three enterprise goals were about customers: identifying new customers and existing customers (who might buy other financial products). Increasingly, social media data provides banks with opportunities to understand customers life changes: children about to go to college (who might need loans), moves to other parts of the country (mortgages), and more. Exhibit III: Banks Most Important Enterprise Goals for Big Data Importance of Enterprise-Level Goals for Banking/Financial Services Companies' Big Data Initiatives (Scale of 1-5, 1=Not at all important, 3=Moderately important, 5= Very high importance) Identify new customers who might buy our products or services Identify which of our existing customers might buy our current products or services Identify ways to reduce error/quality problems in the business Identify whole new product/service opportunities Identify ways to reduce costs in the business Identify improvements to existing products or services that would make them more appealing to customers Identify existing customers that we shouldn t market to because they are unprofitable or less profitable Reduce problems in our distribution processes Reduce problems in our service operations processes 4.05% 3.93% 3.93% 3.79% 3.75% 3.68% 3.35% 2.93% 2.75% from Leveraging Large Volumes of Digital Data 7

8 Banks customer relationship management (CRM) systems of the past have lacked the data that is now available about customers social and professional circles. Much of this data can now be found on social networks. Banks can collect and mine this data while complying with privacy regulations (which can vary significantly from country to country). The second question that sheds light on banks Big Data spending priorities asked respondents to indicate their investments in 10 functions. (See Exhibit IV.) The three functions getting the highest proportion of investments were marketing (17% of total Big Data spending), sales (16%) and customer service (14%). Marketing and sales investments in Big Data help banks improve the effectiveness of their demandgeneration programs, besides enabling them to take advantage of up-sell and cross-sell opportunities. Given that these investments can directly improve the top and bottom line, these two functions account for the biggest proportion of Big Data spending. Exhibit IV: Customer-Facing Functions Get the Biggest Pieces of the Big Data Spending Pie How Banking/Financial Services Respondents Allocated Their 2012 Spending on Big Data by Function (Compared with All Industries Surveyed) Marketing Customer Service Sales Service Operations IT Function R & D / Product Development / Product Engineering Finance / Accounting / Risk Management % 11.11% 12.12% 11.28% 9.88% 7.72% 8.02% 14.99% 16.86% 13.27% 14.26% 15.21% 16.39% Other Functions (e.g., Procurement, Legal, etc.) Human Resources 5% 5.23% 13.07% 11.70% 2% 5% 10% 15% 20% All industries Banking / financial Services Industry 8 from Leveraging Large Volumes of Digital Data

9 The third question focusing on banks Big Data priorities asked respondents which activities within the business functions had the highest potential to benefit from Big Data. Across all functions, banks rated two activities higher than all the others: analyzing customer behavior on the website (to see what pages are most and least useful, and so on) and identifying trends in call center inquiries. The benefits of both were rated 4.25 on a scale of 1-5. By business function, the activities viewed as having the greatest potential benefits from Big Data were: Marketing: Monitoring customer and market perceptions of the bank, and comparing prices with competitors (3.71). As JPMorgan Chase s CIO Chiarello said, Digital marketing will become a significant thing in banking. 5. One-to-one marketing that s the big promise, and that s where competitive advantage will be played out in lots of industries over the next five years, Steve Ellis, EVP and group head of Wells Fargo s Wholesale Services Group, said to The Wall Street Journal, adding, And if you don t figure it out, you re not going to be best in class. 6 Sales: Determining which customers to avoid (4.00) and identifying customers with the greatest value (3.88). Big Data will help banks reduce bad debts from bad loans and credit card fraud. We should be able to be a better credit bureau than the credit bureaus, said JPMorgan Chase CIO Chiarello. 7 Customer service: Analyzing website customer behavior and trends in call center inquiries (4.25) Finance: Measuring risk (3.64)is a function, in part, of having better data and insights on where fraud could occur, a key concern of every bank. Said one former Big Data director at a large bank, The ability to build models on the population is a massive game changer because now as a bank, I can think of eliminating fraud. I can build a model looking at every incidence of fraud going back five years for every single person, rather than sampling it now, building a model, realizing there is an outlier that breaks the model, and then rebuilding the model. Those days are over. Operations: Supply planning (3.5) from Leveraging Large Volumes of Digital Data 9

10 Exhibit V below shows how banks and financial services orgnizations respondents in marketing, sales, customer service, finance/accounting/risk management, and operations rated the potential benefits of Big Data in a number of activities in their functions. Exhibit V: Where Banks See the Gold in Big Data How Banking/Financial Services Companies Rate the Potential of Big Data for Improving Marketing, Sales, Customer Service, Operations and Finance Activities (Scale of 1-5, 1= No Benefits, 3= Moderate Benefits, 5= Very High Benefits) Analyzing behavior of customers using the company s website to see which pages are most and least useful (Customer Service) Identifying the trends in customer inquiries (e.g. to better allocate service personnel to peak times) (Customer Service) Identifying patterns in customer complaints, both internal (in the call center) and external (Customer Service) Determining which customers to avoid (Sales) Identifying customers with the most value/potential value (Sales) Determining optimal sales approaches/techniques (Sales) Discerning trends in customer usage of the company s products/services (Customer Service) Determining optimal sales offers (Sales) Monitoring customer and market perceptions of the company (Marketing) Comparing prices with competitors (Marketing) Pricing (Sales) Measuring risk (Finance/Accounting/Risk Management) Personalizing search results on a company s website (Marketing) Monitoring and improving the customer experience on the web or mobile channel (Marketing) Tailoring marketing campaigns and promotional offers (Marketing) Determining what other purchases customers may be interested in and making recommendations to those customers (Sales) Determining optimal sales messages (e.g. from sales wins and losses) (Sales) Sizing and structuring sales territories (Sales) Analyzing behavior of visitors to company s e-commerce site to see what they are most and least interested in (Sales) Identifying cross-selling opportunities (Sales) Supply planning (Operations) Budgeting/forecasting/planning (Finance/Accounting/Risk Management) Determining customer value (Marketing) Marketing to consumers based on their physical location (Marketing) Pricing (Marketing) Discerning customer needs for new products/services (Marketing) Determining financing amounts for customers (Finance/Accounting/Risk Management) Identifying bad credit risks (Finance/Accounting/Risk Management) from Leveraging Large Volumes of Digital Data

11 The ROI on Big Data Most banking survey respondents told us their companies had already benefited from Big Data. About 86% said their Big Data initiatives had already improved the quality of decision-making. When asked to project the return on their Big Data investments in 2012, the average ROI was expected to be 43.7%. To be sure, it is not easy to tie spending on Big Data to increases in sales and profitability. However, banks and other financial services firms that are able to do so will find it easier to increase their investments in Big Data. How banks are organizing their Big Data activities The majority of the banking respondents said that their organization had centralized their Big Data people either in the IT department or in a Big Data/analytics group. Half of the respondents said that the professionals who process the data are in the IT group, and another 27% placed them in an analytics group. Only 21% of respondents said that Big Data professionals were a part of a business function. We see a similar trend with Big Data analysts. Two-thirds of respondents put them in a central function: 32% said they are in IT and 34% in a Big Data/analytics group. Only 35% of the analysts are embedded in business functions. Centralizing the Big Data function in IT or an analytics group should help banks break down the barriers to collecting the data they need to help improve business activities. It also helps banks attract data scientists who are scarce and therefore in high demand. Furthermore, it gives them a central place to experiment. In 2012, Wells Fargo formed a Big Data laboratory for business units to test analytics tools. 8 Our view is that a successful Big Data team needs a mix of people with deep knowledge of the business, Big Data technology, and analytics all under one leader. A new role that we see gaining prominence in banking is the Chief Data Officer. Such leaders can help banks break down the barriers that prevent business units and functions across the organization from combining their data about customers and other entities, and gaining a holistic picture. The greatest challenges to tapping Big Data are not technological Much has been written about the technological challenges of Big Data especially about the three Vs : the huge volume, variety, and velocity of digital data. Banks have dealt with large volumes of data for years. Hence, it was hardly surprising that banking respondents did not rate the three Vs as the most significant challenge to gaining benefits from Big Data. (See Exhibit VI.) Banking respondents, however, rated two challenges that are not technological as more challenging: getting business units to share information across organizational silos, and determining what kinds of data to use for different business decisions. The data silo issue is one that banks have grappled with for years. JPMorgan Chase s CIO told a reporter that the financial institution has found it challenging to combine all the digital data it has on each and every customer ranging from their mortgage loans and wealth management accounts to checking accounts and more. The process of combining data from different sources within an organization can take a long time. For example, Wells Fargo & Co. began integrating numerous information systems five years ago and has now completed the integration. 9 from Leveraging Large Volumes of Digital Data 11

12 Determining what kinds of data to use for certain business decisions is not straightforward, particularly for emerging unstructured data such as digital text and video from social media. For some financial services experts in Big Data, that is a bigger issue than coming up with the magic algorithm. Said a former managing director of Big Data at Bank of America: The lesson we learned is that algorithms are no longer proprietary which problem you apply the algorithm to is now the important thing. That is a massive game changer because people for the longest time have spent their time thinking about writing an algorithm that nobody has and protecting it. 10 Having a Chief Data Officer at a high level in the organization, as well as a central Big Data group, is crucial to addressing both challenges. Banks that can get the organizational structure for their Big Data activities right will be able to leverage their digital data much faster and realize much higher ROI. Exhibit VI: Dealing With Organizational Silos, Not Technology, is the Biggest Challenge of Big Data Greatest Challenges for Banking/Financial Services Companies for Getting Value from Big Data (Scale of 1-5 1=Not at all a Challenge, 3=Moderate Challenge, 5=Very High Challenge) Getting business units to share information across organizational silos Determining what data (both structured and unstructured, and internal and external) to use for different business decisions Being able to handle the large volume, velocity and variety of Big Data Getting top management in the company to approve investments in Big Data and its related investments (e.g. training, etc.) Keeping the data in Big Data initiatives secure from external parties Building high levels of trust between the data scientists who present insights on Big Data and the functional managers who make decisions Re skilling the IT function to be able to use the new tools and technologies of Big Data Finding and hiring data scientists who can manage large amounts of structured and unstructured data and create insights Putting our analysis of Big data in a presentable form for making decisions (e.g. visualization/visual models) Finding the optimal way to organize Big Data activities in our company Understanding where in the company we should focus our Big Data investment Getting functional managers to make decisions based on Big Data rather than on intuition Getting the IT function to recognize that Big Data requires new technologies and new skills Determining what to do with the insights that are created from Big Data. Keeping the data in Big Data initiatives secure from internal parties Determining which Big Data technologies to use from Leveraging Large Volumes of Digital Data

13 Conclusion Perhaps more than most other industries, banks and other financial services companies have major opportunities to capitalize on Big Data. They manage enormous amounts of digital data and have therefore honed significant skills in this across the years; and their customers increasingly want to interact with them digitally and remotely, through their mobile devices. However, the silo issue that has thwarted many banks CRM efforts in the past will be a major barrier to capitalizing on Big Data. Centralizing Big Data activities, as most banks have already done, is the first big step. The next step is to have a leader for these activities who reports high in the organization and who can eloquently explain the potential of Big Data in simple but compelling terms. Such Big Data leaders whether they are called Chief Data Officers or by any other term will need to be effective at recruiting data scientists who possess strong mathematical and analytical skills. Further, these leaders will have to build effective bridges to the business unit and functional leaders, and help them understand how their top and bottom lines can be improved by far better fact-based decisions. Last, staying on top of fast-changing Big Data technologies, as well as experimenting with the continually increasing streams of new digital data that are coming on board, is crucial. The era of Big Data in banking has just begun. In fact, Bank of America estimated last year that it analyzes perhaps 1% of its 65 petabytes of data on customers. And even then, there s too much of the looking back and not enough of the looking forward, said a bank marketing manager. 11 Banks that can master all of these tasks will put themselves in an advantageous position over the rest of this decade. Research Process and Demographics TCS surveyed 127 respondents from banks and other financial services firms in the U.S., Europe, Asia-Pacific and Latin America. We collected responses from 643 managers across 12 industries, with the banking/financial services sector accounting for 20% of the total respondents. This report focuses on the banking/financial services sector results of the survey. The complete report can be found at default.aspx We surveyed banking executives from around the world. Of those that reported implementing Big Data initiatives in 2012, the largest percentage (40%) of financial services survey respondents were based in the U.S., followed by a slightly smaller percentage (38%) in Europe (UK, Germany, and Netherlands). Around 12% of respondents were from Asia-Pacific (India, Japan and Australia), and 9% were from Latin America (Mexico and Brazil). Nearly half (45%) were in financial institutions with annual revenue of $10 billion or more. Another 43% had revenue between $1 billion to $10 billion. By Big Data, we refer to the phenomenon of vast amounts of digital data structured and unstructured, and internal and external that many large companies must process and analyze rapidly today. This is especially the case in the financial services industry, given that financial products -- and access to them -- are a digital endeavor. This is not altogether new in banking; the industry has always had to manage huge volumes of data, as Wall Street traders, managers of ATM networks, executives at credit card processing companies, and many other banking managers can confirm. from Leveraging Large Volumes of Digital Data 13

14 To respond effectively to customers, efficiently target and market their products and services to the most likely prospects, monitor service quality, and gain other competitive advantages, banks must be proficient at processing and analyzing enormous volumes, varieties and velocities of digital data. (Endnotes) References: 1 The Wall Street Journal, Banks Using Big Data to Discover New Silk Roads, Feb. 6, ibid 3 A. Foege, Bank Data Cashes in on Customer Feelings, CNBC.com, 4 Abhishek Mehta, Hadoop World conference, Mehta_of_Bank_of_America_on_Big_Data_and_the_second_industrial_revolution 5 The Wall Street Journal, Banks Using Big Data to Discover New Silk Roads, Feb 6, ibid 7 ibid 8 A. Foege, CNBC.com article 9 ibid 10 ibid 11 SAS Institute 2012 report, Aditya Bhasin, Big Data for the Next Big Idea in Financial Services, Next%20Big%20Idea%20in%20Financial%20Services.pdf 14 from Leveraging Large Volumes of Digital Data

15 The Emerging Big Returns on Big Data from Leveraging Large Volumes of Digital Data 15

16 About Tata Consultancy Services Ltd (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering TM and assurance services. This is delivered through its unique Global Network Delivery Model, recognized as the benchmark of excellence in software development. A part of the Tata Group, India s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India. For more information, visit us at 16 IT Services Business Solutions Consulting All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2013 Tata Consultancy Services Limited from Leveraging Large Volumes of Digital Data TCS Design Services I P I 06 I 13