Developing the “Moneyball Advantage” in Banking Turning Big Data into a Competitive Differentiator By Dr Aaron Sum, Alliance Bank Malaysia Berhad (Presented at the Asian Financial Services Congress, Feb 23 - 24 2012, Marina Bay Sands, Singapore, Organized by IDC Financial Insights) Introduction and Synopsis The rapid growth of data in recent years, commonly referred to by industry observers as the “Big Data” phenomenon, presents both opportunities and challenges. As companies seek to manage, analyze and harness the insights from these substantially larger sets of data, they are not only confronted with the growing volume of data from their existing applications, but are also faced with new varieties of unstructured data such as those from social networking tools and mobile technologies. Furthermore, the rate of change of new data formats over the past 5 years has been unprecedented. The amount of global data is now projected to more than double every two years, with new sources such as Facebook generating 30 billion pieces of content every month (1). In financial services, a similar trend prevails; there are now 10,000 payment card transactions per second globally. In 2010 alone, 210 billion electronic payments were generated worldwide, and this is projected to double by the end of the decade (2). In the book “Moneyball” (now popularized via the movie of the same name), the author Michael Lewis recounts how the general manager of Oakland Athletics (a baseball team in California) used statistical analytics to find undervalued talent to take on teams like the New York Yankees. Lewis details how statistician Bill James showed that people overlooked the information that would reveal which strategies would be most effective to compete and win in baseball. The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century is subjective and often flawed. As seen from “Moneyball”, analytics, when harnessed to its full potential, can serve to ‘level the playing field’ and enable even the smallest industry players to take new ground and win market share. Given the current climate of protracted slow growth and “hyper competition”, analytics is a key to uncover growth and optimization opportunities. In this context: • How can financial institutions effectively leverage big data analytics to develop the “Moneyball” advantage? • How would banks use big data to find new revenue streams, maximize sales effectiveness, optimize costs and even forecast market trends? • What key steps should banks take to build their analytical capabilities? The conference paper addresses the key issues above and shows the path that banks can take to generate competitive advantage and tangible business value via the application of hypotheses-driven analytics.

2. Turning “Big Data” into a
Competitive Differentiator
Dr Aaron Sum
Senior Vice President, Head of Strategy & Analytics
(SME Banking)

3. Agenda
 “Big Data” in Banking: Opportunities and Challenges
 Recent Trends in “Big Data” Analytics
 Turning Insights into Business Value: The “Moneyball” Advantage
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4. The amount of global data is projected to
more than double every 2 years
“Data, data, everywhere ...” (1,2) Acceleration of Data Growth
• Companies capture trillions of bytes of
information about their customers,
suppliers, and operations
• Millions of networked sensors are being
embedded in the physical world in
devices such as mobile phones and
automobiles, sensing, creating, and
communicating data
• Multimedia and individuals with smart
phones and on social network sites will
continue to fuel exponential growth
(1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011
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(2) “Data, data, everywhere”, The Economist, Feb 2010

5. In financial services, we are now seeing
new waves of data growth
Recent Headlines: Data Growth in Financial Services
 The New York Stock Exchange creates 1 terabyte of data
Big Data Defined
per day vs. Twitter feeds that generates 8 terabytes of
data per day (or 80 MB per second) .
“Big data" refers to  10,000 payment card transactions per second around the
the management, world.
access, and analysis
 210 billion electronic payments generated worldwide in
of substantially larger
2010. This is expected to double by the end of the
sets of (typically
decade.
unstructured) data
than had been  Between 2009 and 2014, the total number of US online
conventionally banking households will increase from 54 million to 66
million.
possible until
recently.  46% of financial services CIO‟s are exploring the
potential of could computing, up 33% from 2010.
 10x growth in Market Data volumes between 2007-2011
and growing.
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Source: Information Week, American Banker

6. The Banking sector is poised for
substantial gains from the use of big data
Data intensity by sector Spectrum of „big data‟
Examples:
• Transactional data
• Lifestyle-related information
• Behavioral data
• Demographics
• Geospatial information and location
intelligence on customers
• Online and social media
interactions
• Mobile (smart-phone) usage trends
The quest for a true “360
Degree Customer View”
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Source: (1) “Big Data: The next frontier for innovation, competition & productivity”, McKinsey Global Institute, June 2011

7. However, banks must navigate the
complexity, variety and velocity of “big data”
Big Data Challenges
• Growing volume of unstructured data from banks‟ current applications
as well as the newer technologies being adopted
Complexity
• Adds another layer of complexity to the elusive “360 Degree
Customer View” which banks have been pursuing
• Mobile technologies, social networking tools, etc are significantly
increasing the stock of unstructured data within the banks
Variety
• The rate of change of new data formats over the past 5 years have
been unprecedented; the trend is expected to continue
Velocity
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8. “Big Data” Analytics, when harnessed
correctly, can be a substantial competitive edge
From Traditional Sources of … to Analytics-Driven
Competitive Advantage … Competitive Advantage
Differentiation • Product
(e.g. product, price, • Price
service) • Cost
• Service
• Customers
OR
Multiple
Business strategy sources of
Cost Leadership competitive
Analytics advantage
“Big data”
“Trade off between low cost or “Right product, price, service
focused differentiation, or hybrid levels (at the right cost), for
approach” the right customer”
Analytics must move from the „fringe‟ to the „core‟ of all strategic and tactical
business decisions, to develop this competitive advantage
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9. Given the current climate of protracted “slow
growth” and “hyper competition”, analytics is key
to uncover growth / optimization opportunities
Current Challenges Key Imperatives
• Protracted “Slow Growth”: • Finding new market segments and
Increasingly challenging for banks to revenue streams
sustain revenue momentum; cost
optimization begins to take centre-stage
• Maximizing sales and marketing
effectiveness
• “Hyper-competition” and continued
margin compression: Competition
continues to intensify as margins are • Optimizing costs and existing resources
eroded
• Risk-based pricing
• Ability to forecast market trends, gauge
customer sentiment and adapt business
strategies quickly
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10. Agenda
 “Big Data” in Banking: Opportunities and Challenges
 Recent Trends in “Big Data” Analytics
 Turning Insights into Business Value: The “Moneyball” Advantage
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11. Recent Trends:
“Big Data” Analytics • Sales effectiveness
• Opening up new target segments
Granular Micro-
1 Targeting of • New product response
• Campaign effectiveness
• New customer value proposition customers / segments • Brand health
Partnerships with Sentiment Analysis to
6 Analytics Specialists / 2 gauge „real time‟
Providers response to campaigns
Recent Trends
“Big Data
Analytics”
Predictive staff Trend Forecasting &
5 scheduling to optimize 3 Market Research via
costs novel data sources
• Cost optimization Proactive Monitoring to • Forecasting trends
4 detect early „fault
triggers‟
• Service quality
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• Proactive customer management

12. Case example in “Big data” analytics
Example: Large Spanish Bank
Granular Micro-
1 Targeting of customers Micro-targeting via personalized campaigns
/ segments
• The bank adopts an event-driven marketing approach on retail and SME
customers
• Hundreds of automated algorithms tested during the last 10 years to
produce 500 personalized campaigns every week
 Multi-year effort of customer data collection to refine customer potential value
 Strong use of customer potential value to prioritize commercial effort.
Business Value Generated
• Cross-selling index of 6.45 vs. Spanish average of 3
• Churn rate of 6.90% vs. Spanish average of 14%
• Service level index of 76,8 vs. Spanish market index of 70.5
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Source: Bank Annual Reports, Analyst Reports

13. Hundreds of automated algorithms (continually tested &
refined), generate thousands of personalized campaigns
every month, pushed to customers’ mobile phones
Example of “real-time” offer: Proposal for a personal loan just after the purchase event
The customer buys an LCD TV with If the customer is interested, The operation ends when the customer
his credit card and the Bank sends he replies with a code receives the confirmation message showing
him the following message that his purchase has been financed
1 2 3
“The Bank” TAJ 1 50 Operation
finances until successfully done.
xx/xx/xx your VISA We have financed
purchases of 1.200 your VISA purchase
EUROS in 12 quotas of 1.200 EUR in 12
of 107,18 EUROS payments of 107,18
per month. EUR per month.
To finance it, Check this
please answer TAJ transaction in
1 and the sum of xxbank.com
coordinates B1 +
E2
Matrix Card (Tarjeta de claves): It is a card containing letters and numbers from which, each time a
customer needs to perform an operation (e.g. a money transfer), the Bank asks for a code 13

14. Case example in “Big data” analytics
Example: Progressive Insurance
Granular Micro-
1 Targeting of customers Opening up new target customer segments
/ segments
• Progressive defines narrow groups of customers (or “cells”)—for example,
motorcycle riders older than 30 with no previous accidents, a college education,
and a credit score higher than a certain level.
• For each cell, the company performs regression analysis to identify the factors
that most closely correlate with its loss experience.
• They set prices for each cell they believe will enable them to earn a profit
across a portfolio of customer groups.
• A simulation model is used to test the financial implications of these
hypotheses.
Business Value Generated
• Enable targeting of new segments based on deeper understanding
of risk-returns (e.g. higher risk segments that were previously
„blacklisted‟)
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Source: “Competing on Analytics”, Thomas H Davenport, Don Cohen and Al Jacobson

15. Case example in “Big data” analytics
Example: Large Australian Bank
Sentiment Analysis to
2 gauge „real time‟ Tracking social media sentiment towards campaigns
response to campaigns
• The bank had started its social media activities like most banks in the region:
it launched a Facebook page, created a twitter account, as well as its
LinkedIn profile.
• However, it soon realized that social media was not only about presence but
also about engaging with customers. At this stage, the bank was only using
social media as a unidirectional marketing channel — in a similar way to how
traditional marketing channels were normally used.
• However, the bank recognized that social media presented great
opportunities for the organization, since millions of conversations are
constantly taking place, and some of those were about their bank.
Application of Analytics
• Put in place social media analytics tool to gauge sentiment on bank‟s overall
brand perception, as well as to specific marketing campaigns
Examples: ING, Citi, SunTrust
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Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011

16. Leading banks are embracing online & social media
analytics of customer sentiment and opinions to gauge
response to new products and campaigns
Example: Social analytics dashboard Observations
Leading banks are
already turning to
social analytics to
gauge sentiment
towards key
initiatives such as:
• New Product
launches
(e.g. ING)
• Marketing
campaigns
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Source: IDC “Journey into Big Data: From Transactional Data to Big Data Analytics”, 2011

17. Case example in “Big data” analytics
Example: UBS Investment Bank
Trend Forecasting &
3 Market Research via Forecasting sales trends using satellite data
novel data sources
• UBS Investment Research issued its earnings preview for Wal-Mart's second
quarter, which publicly revealed that UBS had been using used satellite
services of private-sector satellite companies to gather the comings and
goings of the parking lots at Wal-Mart stores. “UBS proprietary satellite
parking lot fill rate analysis points to an interesting cadence intra-quarter and
potential upside to our view,” the report read
• UBS analyst Neil Currie had been looking at satellite data on Wal-Mart during
each month of 2010, and he‟d concluded that there was enough correlation
between what he was seeing in the satellite pictures of Wal-Mart‟s parking
lots to the big-box chain‟s quarterly earnings, that he was ready to
incorporate that data into UBS‟ report on Wal-Mart
• By counting the cars in Wal-Mart‟s parking lots month in and month out,
Remote Sensing Metrics analysts were able to get a fix on the company‟s
customer flow. From there, they worked up a mathematical regression to
come up with a prediction of the company‟s quarterly revenue each month.
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Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 2010

18. UBS found greater correlation from its satellite
data projections than its traditional statistical
methods
More Accurate Forecasting
Novel application of “Big Data”
• In the second quarter, the
satellite analysts had spotted a
surge in traffic to Wal-Mart
stores during the month of
June, which was 4 percent
ahead of the same month a
year ago.
• That, they speculated, was
driven by an aggressive Wal-
Mart price rollback marketing
campaign that brought a lot
more customers into the stores
• Because they could see that
traffic showing up in the
parking lots, the satellite
analysts came up with a
much different projection for
the company‟s quarterly
earnings in the second
quarter than the UBS team
did using traditional
methods.
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Source: CNBC “New Big Brother: Market-Moving Satellite Images “, Aug 2010

19. Case example in “Big data” analytics
Example: Bank of America
Proactive Monitoring to
4 detect early “customer Contact centre sentiment analytics
service issues”
• Sentiment analytics to provide insight into a customer‟s feelings about the
organization, its products, services, customer service processes, as well as
its individual agent behaviors.
• Sentiment analysis data is then used across an organization to aid in
customer relationship management, agent training, and to help identify and
resolve troubling issues as they emerge
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Source: “State of the Art: Sentiment Analysis”, Nexidia 2009

20. Case example in “Big data” analytics
Example: Bangor Savings Bank (USA)
Predictive staff
5 scheduling to optimize Predictive model to optimize branch staffing
costs
• Predictive model that forecasts teller staffing based on forecasted transaction
volumes.
• The tool uses business intelligence to analyze transaction data that's
collected. Reports are produced each month on transaction workloads, labor
cost per transaction, and salary and benefit expenses matched against
transactions; these reports are updated hourly and coupled with projections.
• The result is a benchmark that a bank can use to match an expected level of
service. Bangor Savings Bank is using it to execute mundane yet time
consuming scheduling challenges, such as computing part-time teller hours,
or moving tellers around during the day to take care of other tasks based on
customer traffic.
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Source: “Banks Turn to Staff Scheduling Software to Cut Costs”, American Banker, Jan 2012

21. Case example in “Big data” analytics
Example: Cardlytics
Partnerships with
6 Analytics Specialists / Transaction-driven marketing using propensity models
Providers
• Cardlytics combines transaction marketing with daily deal couponing and
online banking to help banks provide a new service to customers.
• It plays in the "merchant funded rewards" space, a nascent industry where
banks allow merchants to offer customers rewards and discounts
through the online banking channel, based on customer card
transactions.
• Banks never share any personally identifiable information on Cardlytics'
platform. It looks at anonymized transaction data only and matches
merchant offers based on a forecasted propensity to buy.
• Merchants only pay if the offers are successfully redeemed, and Cardlytics
shares that revenue with the banks.
• In Cardlytics' model, banks present offers to customers via electronic
statements. But users will soon be able to activate offers via the ATM and
through social media sites like Facebook and Twitter
To date, 100 – 200 financial institutions partner with Cardlytics to offer
this service to their customers (e.g. PNC Financial Services Group)
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Source: “Cardlytics”, American Banker, Dec 2011

22. The rise of niche analytics firms such as Cardlytics that
can be valuable partners to banks seeking to enhance
their customer value proposition
Transaction Driven Marketing
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Source: Cardlytics website

23. Agenda
 “Big Data” in Banking: Opportunities and Challenges
 Recent Trends in “Big Data” Analytics
 Turning Insights into Business Value: The “Moneyball” Advantage
23

24. Developing the “Moneyball” Advantage:
Analytics-driven strategies vs. Conventional wisdom
Using Analytics to Develop a
Winning Advantage
 Small market Oakland A‟s general
manager Billy Beane success story as
he uses statistical analysis to find
overlooked talent to take on teams like
the New York Yankees
 Author Michael Lewis details how
statistician Bill James showed that
people overlooked the information that
would reveal which strategies would be
Analytics, when harnessed to its full potential, most effective in to compete and win in
can serve to „level the playing field‟ and enable baseball
smaller players to rapidly gain market share  The central premise of Moneyball is
that the collected wisdom of baseball
insiders (including players, managers,
coaches, scouts, and the front office)
over the past century is subjective and
often flawed
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25. Building Competitive Advantage:
Moving up the Analytical Capability Curve
Analytical Capability Curve(1)
Competitive Optimization What is best that can happen?
Advantage
Predictive Modeling What will happen next?
Forecasting What if these trends continue?
Statistical Analysis Why is this happening
Alerts What actions are needed?
Query What exactly is the problem?
Ad-hoc reports How many, how often, where?
Standard reports What happened?
Sophistication of Intelligence
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Source: (1)“Customer Analytics – Cutting a New Path to Growth and High Performance”, Accenture, 2010

26. The Path from Insights to Business Value
6 Guiding Principles
1. Focus on the highest value opportunities
2. Start with key questions and hypotheses, not data
3. “Test, learn and refine”
4. Build internal analytics capability
5. Instill an analytics-driven culture to inform all strategic decisions
6. Augment with specialist analytics providers (where required)
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27. Contact Information
Dr Aaron Sum
Senior Vice President, Head of Strategy & Analytics
(SME Banking)
aaronsww@alliancefg.com
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