Mor Sela IKNS 4304 Assignment 1 Analytical Assessment v2.docx Columbia University 1 Banking Industry Analytical Assessment Mor Sela, IKNS 4304, Assignment #1, February 9 th 2014 Introduction In the 1980s and 1990s, IT systems transformed virtually every single bank process. Today, banks have that rare opportunity to reinvent themselves again—with data and analytics. “Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics,” says Toos Daruvala, a director in McKinsey’s New York office. “This will be a differentiator for some period of time.” 1 This paper assesses the analytical sophistication of the banking industry. It describes the various analytics opportunities for banks and provides examples of how banks actually take advantage of these opportunities. Industry’s Approach to Analytics and Decision Making Many banks have been successful in using analytics technologies and practices for quite some time now. Data analytics provides very big opportunities for banks. Almost every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data analytics. Typically, the first applications that use analytics are in marketing lead generation and in risk management. Both are disciplines that have historically used information pretty well. But we are now at the next frontier in terms of using data analytics to drive revenue generation through improved marketing effectiveness, fraud prevention, and reduced risk of commercial lending. What kind of data do managers look at when they make decisions? Banks look for a variety of data. Most recently, there is a focus on getting data that will allow banks to create profiles for their customer and prospect, not only based on their demographics and credit score data, but also their behavioral data. This includes attributes such as spending patterns, investment patterns (including level of risk taking), income patterns, saving patterns, borrowing patterns, as well as social media activity. JPMorgan Chase, for example, the largest commercial bank in the U.S., generates a vast amount of credit card information and other Figure 1 Sources of Customer Data (source: bigdatastartup.com)
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Mor Sela -‐ IKNS 4304 -‐ Assignment 1 -‐ Analytical Assessment v2.docx Columbia University 1
Banking Industry Analytical Assessment Mor Sela, IKNS 4304, Assignment #1, February 9th 2014
Introduction In the 1980s and 1990s, IT systems transformed virtually every single bank process. Today, banks have that rare opportunity to reinvent themselves again—with data and analytics. “Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics,” says Toos Daruvala, a director in McKinsey’s New York office. “This will be a differentiator for some period of time.”1
This paper assesses the analytical sophistication of the banking industry. It describes the various analytics opportunities for banks and provides examples of how banks actually take advantage of these opportunities.
Industry’s Approach to Analytics and Decision Making Many banks have been successful in using analytics technologies and practices for quite some time now. Data analytics provides very big opportunities for banks. Almost every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data analytics. Typically, the first applications that use analytics are in marketing lead generation and in risk management. Both are disciplines that have historically used information pretty well. But we are now at the next frontier in terms of using data analytics to drive revenue generation through improved marketing effectiveness, fraud prevention, and reduced risk of commercial lending.
What kind of data do managers look at when they make decisions?
Banks look for a variety of data. Most recently, there is a focus on getting data that will allow banks to create profiles for their customer and prospect, not only based on their demographics and credit score data, but also their behavioral data. This includes attributes such as spending patterns, investment patterns (including level of risk taking), income patterns, saving patterns, borrowing patterns, as well as social media activity.
JPMorgan Chase, for example, the largest commercial bank in the U.S., generates a vast amount of credit card information and other
Figure 1 -‐ Sources of Customer Data (source: bigdata-‐startup.com)
Mor Sela -‐ IKNS 4304 -‐ Assignment 1 -‐ Analytical Assessment v2.docx Columbia University 2
transactional data about U.S. consumers. Late 2012, it began to combine that database, which includes 1.5 billion pieces of information, with publicly available economic statistics from the U.S. government. Then it used new analytic capabilities to develop proprietary insights into consumer trends, and offer those reports to the bank’s clients. This allows the bank to break down the consumer market into smaller and more narrowly identified groups of people, perhaps even single individuals. And those new reports can be generated in seconds, instead of weeks or months.
Steve Ellis, executive vice president and group head of the Wells Fargo Wholesale Services Group says, “the behavioral analysis stuff is coming” in the next five years. He warns, however, that there’s still a lot to understand for the banks to learn before they can “get to 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. And if you don’t figure it out, you’re not going to be best in class.” 2
What kind of data is available? There is no shortage of data for banks. According to Alacer Group3 US banks currently have 1 Exabyte (1 billion gigabytes) of stored data. The sources of this data includes, bank transactions, credit card activity, web interactions, call logs, customer bank visits, and social media activity.
To whom and how do they have to justify their decisions? I didn’t find good sources to address this question. Obviously the answer would vary depending on the size of the bank, its culture, and its policies. The range of bank sizes is tremendous, from giant banks as JP Morgan Chase with 255 thousand employees and $53 billion in annual revenues down to small regional banks such as First Federal of Northern Michigan Bancorp with 69 employees and $8.4 million in annual revenues. That said, it is evident that banks that successfully implement “information-‐based strategy” such as Barclays and Capital One, had a strong commitment to justify decision with data by the senior leadership of the organization, including the CEO.
How does data drive strategy? Here are few examples of domains in which banks use data to drive strategies:
• Product Strategy (by better understanding customer needs) • Customer Support Strategy (using predictive analytics of social media customer
sentiments, purchasing power and other behavioral data) • Marketing Strategy (by better identifying desired target customers) • Credit Risk strategy (by implementing better analytics of credit bureaus data) • Staffing strategy (by analyzing traffic patterns and transaction times)
See Appendix 1 for more details.
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Analytical Competitors Attributes4
Which of these four attributes do the best banks exhibit? Distinctive Capability:
Capital One was one of the early leading US banks to implement strategic analytics. By 2005 they have been conducting more than 30,000 experiments a year, with different interest rates, incentives, direct mail packaging, and other variables. Through this analytical approach to marketing, Capital One was able to identify and serve new market segments before its peers could.5
Enterprise-‐wide Analytics:
Bank of America attributed its success in analytical around asset and interest-‐rate risk exposure to the fact that risk was managed in a consistent way across the enterprise. Many other banks have been limited in their ability to assess the overall profitability or loyalty of customers because different divisions have different incompatible ways to define and record customer data. 6
Senior Executive Commitment:
As early as in 1998, innovative banks such as UK based Barclays started instituting “information-‐based customer strategy”. Their consumer finance organization has implemented a five-‐year plan to build the unit’s capabilities for analytical competition. This long-‐term planning to analytics could not have been implemented without clear evidence of commitment from Barclays’ most senior executives.7
Large Scale Ambition:
Singapore’s DBS Bank is the largest bank in Southeast Asia. It is the dominant retail bank in Singapore, and also has a growing presence in China and South Asia. Just several years ago, the bank was lagging technology-‐wise, but with new technology and operations leadership in 2008, the bank has decided to transform itself and leverage analytics (as well as other technologies) to remake its relationship with customers and its operations. As David Gledhill, group executive and head of technology and operations, who led this transition testifies, “The most important thing was to get the culture shift right. That’s what we worked on for the first two or three years. Obviously that’s a journey. The cultural messages we gave were some of the most relevant ones to get the thinking to shift. DBS prioritized analytics investments and used analytics to improve every possible part of their business.8
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Figure 2 -‐ The Four Pillars of Successful Analytical Competitors
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Analytical Competition Assessment
Benchmarking Vs. Other Industries According to a report by The Economist Intelligence Unit and IBM Institute of Business Value9 (based on interviews of 1,168 executives across nine industries in 64 countries), the banking industry is more advanced than other industries in its adoption of enterprise-‐wide predictive analytics (“Anticipate”) and at about average when it comes to descriptive analytics (“Listen”). That said, as depicted in Figure 1 below, the average adoption rate for both analytics practices is still under 50% which means there is certainly an opportunity for innovative banks to improve and develop competitive advantage using analytics.
Figure 3: Adoption of Listen and Anticipate competencies across the enterprise or full value chain by industry.
Analytical Competition Stage
From the above report and from reading many case studies, I would assess that most banks can be classified between Stage 3 and Stage 5 of the Analytical Competition Model. Some banks such Capital One, Barclay, and DBS, have truly implemented “information-‐based strategy” and can be certainly be categorized as Analytical Competitors. The top USA banks, JPMorgan Chase, Bank of America, Citi, and Wells Fargo – can be categorized as Analytical Companies as they invest significantly in analytics, but have not made this a visible strategic differentiation yet. It seems that still a large number of banks (mostly the smaller ones) can be categorized as at the Analytical Aspirations stage, given they still weren’t able to fully execute on their vision and significantly leverage analytics as a competitive advantage.
Figure 4 below maps banks analytics level on a scale of Breath (analytics use cases) and Depth (level of sophistication). It also provides example of five banks that master at least on use case.
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Industry Leader Analysis (what would you recommend as a goal for this organization?)
As mentioned above, JPMorgan Chase (JPM), the largest commercial bank in the U.S., has started to leverage enterprise-‐wide big data analytics just last year. I believe the bank should aspire to reach Stage 5. Such analytics sophistication could help JPM reverse the recent trend of commercial customers to prefer smaller banks.2 With advanced analytics, the bank could provide more individualized service to its customers, while leveraging its economies of scale.
Conclusion
Relative to other industries, banks have always been high on the maturity curve for employing business analytics to solve business problems. While the implementations are as individual as the companies themselves, three common areas are always of particular focus: customer profitability analytics, risk management and increasing operational efficiency.
But from all the resources referenced below, a common conclusion is that there is still a tremendous untapped opportunity for banks in leveraging business analytics. Just like banks that in the early days used ATMs and later the Internet to create competitive advantage for a few years -‐-‐ innovative banks are going to seize the data analytics opportunity to truly differentiate themselves in the coming years.
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Appendix 1: How Banks put Big Data to Work
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Appendix 2: Examples of How Banks Leverage Data Analytics
Rabobank: Taking Steps into Big Data Analytics10
Background
Rabobank is a Dutch multinational bank. Rabobank started developing a big data strategy in July 2011. After identifying several potential use cases, Rabobank started with a few proof of concepts (POCs) and they first started using only internal data. Next to internal data, Rabobank distinguishes internet data (click behavior), social data (from social networks), public data (from government sources) and trend data. In order to be able to test several big data tools for different use cases, Rabobank decided to build a small Hadoop cluster. This clusters consisted of 16 nodes including 1 master node. A dedicated, highly skilled and a multidisciplinary team was created to start with the big data use cases. The culture among the team members was important for the success of the POCs. In order to stay up to speed, they worked with small and short cycles and most importantly it was allowed to make mistakes as long as the mistakes provided a learning experience. After mastering the small use cases, the objective was to move on to more complex cases.
Analytics Use Cases
One of the use cases was to create an auto-‐complete function for mobile banking. With this feature, users would not have to use their address book anymore. Instead, the system would auto-‐complete account information when a user types an account number. Of course, it should not be possible to view account information of unknown people. Therefore, the system analyzed 3 billion transactions in the financial network. When a search history of 14 months was used, 99% of the accounts had 122 or less unique contra accounts. Thanks to this big data tool, mobile banking has become a lot more customer friendly.
Another use case of the Rabobank was to analyze criminal activities at ATMs. Rabobank found out that the proximity of highways, the season and weather condition increased the risk of criminal activities. Rabobank also used big data to analyze customer data to find the best places for ATMs.
Lesson Learned
According to Harrie Vollaard, innovation manager at Rabobank, creating a big data strategy is not easy and eventually this should be an important part of the overall strategy of the bank.
Rabobank found out that big data technology is ready and not expensive to implement when open-‐source tools are used. The Hadoop cluster that they used delivers high performance with low costs and can be scaled linearly.
For Rabobank, the key to success was the multidisciplinary team and that they embraced uncertainties and accepted mistakes to be made.
There were also challenges. Privacy and data security are big areas of concern. Additionally, data quality was not constant. During the process the Rabobank noticed that it was often unclear who owned the data as well as were all data was stored. Finally, they noticed that specialized knowledge as well as visualizations are very important to drive big data success.
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Scotiabank is the wholesale banking arm of the Canadian Scotiabank Group, with 29 offices and more than 300 relationship managers organized around industry specialties. It offers a wide variety of investment and corporate banking products and services to government, corporate, and institutional clients.
Challenges
The counterparty risk systems that Scotiabank had in place provided overly conservative measures, and could not support a consolidated view of counterparty credit risk (CCR). Scotiabank wanted to efficiently manage capital and credit so that it could conduct more business without increasing overall risk. Previously, traders would have to ask the risk management group to run what-‐if analyses. Risk management would run the trade and provide numbers, but the turnaround was not fast enough to keep pace with moves in the market or client requests. Moreover, instead of a portfolio methodology, some businesses used grid table estimates. Because the firm was relying on very rough approximations to determine credit limit utilizations, the numbers estimated during the day did not always match the numbers run at the end of the day.
Solution and Business Impact
With business analytics software (from IBM), Scotiabank gained a unified solution for measuring and managing counterparty exposures in the front, middle and back office. With access to sophisticated analytics on different types of trades, Scotiabank’s traders can conduct scenario analyses to make the best trading decisions. Traders and the credit group know what a trade will look like and how the exposure increases or decreases with time. The bank can now use its credit lines more efficiently. With the proper measure of counterparty risk, Scotiabank has more efficient utilization of credit lines, which means it can do more business with the same, or lower, limits.
TMB: Increasing Responsiveness to Emerging Customer Needs12
Background
With more than six million customers, TMB Bank is one of Thailand’s largest banks. It offers a comprehensive range of consumer and commercial financial products and services through the Internet and at more than 450 branches, 100 foreign exchange centers and nearly 2,300 ATMs.
Challenges
Experiencing rapid expansion in Thailand’s extremely dynamic consumer and business lending market, the bank needed to learn more about its customers, become more responsive to their requirements by streamlining the process of bringing new products and services to market, and ensure compliance with Thailand’s increasingly complex regulatory environment. The bank sought a solution that would provide effective analysis of customer data, automate and
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accelerate the process of altering business rules, speed new product introductions and reduce risk.
Solution
Escalating customer and regulatory requirements demand that TMB Bank improve its data, process and risk management capabilities. Using an Enterprise Content Management solution and service-‐oriented architecture, the bank now collects current customer credit information from each of its 450 branches and stores that data in a central repository, allowing changes to customer data and risk profiles to be automatically and rapidly assimilated and reported across all banking applications and business units—thus reducing risk exposure and improving profitability.
Business Impact
The solution eliminates data silos, and a modernized business rules management system allows business users to establish new rules on demand, without having to wait for the IT department to manually program each change. With this newfound agility, business units update loan underwriting rules and approval requirements with ease and react more quickly to evolving customer needs as they develop and launch several new financial product and service offerings each month based on actionable customer data.
This translates to:
• Reduced loan processing time from months to approximately two weeks • Reduced nonperforming loan ratio from 12.7 percent to 8.3 percent • Lowered year-‐over-‐year cost-‐to-‐income ratio by 17 percent • Reduced time to market for new products from three months to two weeks
1 McKinsey & Co., How advanced analytics are redefining banking, April 2013, http://www.mckinsey.com/insights/business_technology/how_advanced_analytics_are_redefining_banking (last accessed 2/6/2014) 2 The Wall Street Journal, CIO Journal, Banks Using Big Data to Discover ‘New Silk Roads’, 2/6/2013, http://blogs.wsj.com/cio/2013/02/06/banks-‐using-‐big-‐data-‐to-‐discover-‐new-‐silk-‐roads/ (last accessed 2/8/2014) 3 Alacer Group, Big Data in Banking, 2013, http://data.bigdatastartups.netdna-‐cdn.com/wp-‐content/uploads/2013/08/Big-‐Data-‐is-‐big-‐business-‐in-‐banking.jpg (last accessed 2/8/2014)
4 Davenport, Thomas and (our own ☺) Harris, Jeanne, Competing on Analytics (Harvard Business School Publishing, 2007), 23-‐34 5 Davenport, Thomas and (our own ☺) Harris, Jeanne, Competing on Analytics (Harvard Business School Publishing, 2007), 42 6 Davenport, Thomas and (our own ☺) Harris, Jeanne, Competing on Analytics (Harvard Business School Publishing, 2007), 28
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7 Davenport, Thomas and (our own ☺) Harris, Jeanne, Competing on Analytics (Harvard Business School Publishing, 2007), 31-‐32 8 MIT Sloan Management Review, 1/1/2014, DBS Bank Pumps Up the Volume on its Technology http://media.proquest.com.ezproxy.cul.columbia.edu/media/pq/classic/doc/3176692161/fmt/pi/rep/NONE?hl=&cit%3Aauth=Fitzgerald%2C+Michael&cit%3Atitle=DBS+Bank+Pumps+Up+the+Volume+on+its+Technology&cit%3Apub=MIT+Sloan+Management+Review&cit%3Avol=55&cit%3Aiss=2&cit%3Apg=1&cit%3Adate=Winter+2014&ic=true&cit%3Aprod=ProQuest&_a=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%3D&_s=bW7r6hO7No330txQTOVv%2Bq2eGHs%3D#statusbar=1&zoom=110 (last accessed 2/9/2014)
9 The Economist Intelligence Unit and the IBM Institute of Business Value, Outperforming in a data-‐rich, hyper-‐connected world, 2012, http://public.dhe.ibm.com/common/ssi/ecm/en/yte03002usen/YTE03002USEN.PDF (last accessed 2/7/2014)
10 Big Data Startups, Rabobank Case Study, 2013, http://www.bigdata-‐startups.com/BigData-‐startup/with-‐proof-‐of-‐concepts-‐rabobank-‐learned-‐valuable-‐big-‐data-‐lessons (last accessed 2/7/2014)
11 IBM Scotiabank Case Study, 2012, http://www-‐01.ibm.com/common/ssi/cgi-‐bin/ssialias?subtype=AB&infotype=PM&appname=SWGE_YT_YT_CAEN&htmlfid=YTC03514CAEN&attachment=YTC03514CAEN.PDF (last accessed 2/7/2014)
12 IBM TMB Bank Case Study, 2011, http://www-‐01.ibm.com/common/ssi/cgi-‐bin/ssialias?subtype=AB&infotype=PM&appname=SWGE_ZZ_VH_USEN&htmlfid=ZZC03129USEN&attachment=ZZC03129USEN.PDF (last accessed 2/8/2014)
