A lecture I have at Warwick Business school with my good friend and colleague Abhi Shah. Content has been removed for confidentiality reasons

1. INFORMATION
DRIVEN BANKING
STAVROS APOSTOLOU
(VICE PRESIDENT, ANALYTICS CENTRE OF EXCELLENCE,BARCLAYS UK)
December 2015
Version 1.0
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WE’RE HERE TO TALK
ABOUT OIL RESERVES…
3.1m oil barrelsc80bnrecoverable barrels
We’ve extractedjust130oil tankers
Outof26,000oil tankerstill it dries

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…AND INFORMATION
Fora typical Fortune 1000 company, just a 10% increase indata accessibility
will result in morethan $65 million additional netincome.

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FINANCIAL SERVICES ARE NOT AS
ATTRACTIVE ANYMORE
-
400
800
1,200
1,600
2,000
1991 1994 1997 2000 2003 2006 2009 2012
MARKET CAP GROWTH TRENDLINE OF SELECT
INFORMATION DRIVEN COMPANIES AND GLOBAL
FINANCIAL SERVICES FIRMS (INDEXED TO 1991)
Information
driven
companies
Global
financial
services
Moore’s law
Kryder’s law
Source:“A moneyandinformationbusiness–State offinancialservices(2013)”, OliverWyman

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WHAT YOU MIGHT THINK IS
HAPPENING IN BANKING
$235bn in fines
over 7 years
From just 20 biggest bands
Source:http://graphics.thomsonreuters.com/15/bankfines/index.html?utm_source=twitter
AnnualGDP (2014)
c227bnUSD

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WHAT IS REALLY GOING ON IN
BANKING
Source:http://www.economist.com/news/finance-and-economics/21674778-europes-dithering-banks-are-losing-ground-their-decisive-american-rivals-banking

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SHIFTING ECONOMIC
CENTER OF GRAVITY
Source:TheEconomist,adaptedMGIresearch

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WHAT CHANGES IN
FINANCIAL SERVICES
Automation Simplification
Customer 3.0
Removing
legacy
structures

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DISRUPTING FINTECH FIRMS
Source:http://insights.venturescanner.com/2015/09/04/the-state-of-financial-technology-in-six-visuals/

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WE SHOULD EXPECT OUR
VERY OWN ‘UBER’ MOMENT

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A DIFFERENT VIEW OF THE
BANK
Insurance
Bank
Consumer
Capital markets
Large enterprise
Source:OliverWyman,“Thebankas aninformationbusiness”,(adapted)
Investors
Creditscore
Assets
Investments
Bio/Riskinfo
Retirementplan
Realtime
telematics
Inventory
Balance sheet
Real time M2M
Risk factors
Cash, electronic, debit,
credit, salary
Letter of credit, invoices,
cash, check
Business
Trade/Supply chain
Application data
Balance sheet
Capital raising, FX
Real time M2M
Risk factors

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INFORMATION PRODUCT
AREAS
Publicly
available
information
Insights for
Small
Businesses
Finance &
Treasury
Collection Optimisation
Presents useful facts
and figures about
people and small
businesses in local
communities
SME focused
aggregated data and
actionable insights
service
Provide rich,
integrated financial
insights into all the
accounts held with a
bank
Help clients collect
better from their
customers by being
able to accurately
predict future behavior
Market &
Customer
Insights
Powerful insights on
segments,
motivations and
transactional
behaviors

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VERIFYING YOURSELF
1min 30s
Source:https://gds.blog.gov.uk/category/id-assurance/
“Within search results, information tied to verified
online profiles will be ranked higher than content
without such verification, which will result in most
users naturally clicking on the top (verified) results.
The true cost of remaining anonymous, then, might be
irrelevance.” Eric Schmidt

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YOUR TURN: BREAK OUT
FOR 15 MINS
Background
Your task is to develop your own Analytics Centre of Excellence
in a major retail bank.
Questions
Which factors would you consider to become best-in-class?

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A SIMPLE FRAMEWORK
Analytics centre of
excellence
Your clients
Functions
Decision Layers
Capability areas
Current
Future
Toolkit
Software tools
Frameworks
People
Skills
Behaviors
Connections
Security &
Compliance
Scaling up
Background
Yourtaskis todevelop yourownAnalyticsCentre of
Excellencein a majorretailbank.
Questions
Let’sthinkthrougheach oneoftheseareas

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AN ANALYTICS CENTRE OF
EXCELLENCE STRUCTURE
Customer Value
Management
Pricing & Optimisation Digital
• Target Operating Model
• Proposition Development
• Strategy Analytics
• Marketing Analytics
• Pricing modeling
• Price testing
• New product launch
• Product analytics
• Customer journey analysis
• Digital transformation
• Customer experience
Advanced Data Science
• Machine learning
• Text analytics
• Personalisation
Data management
Mortgages
Lending
Transactions Corporate &
SME
Digital
Savings
Advocacy
Premier &
International
Wealth
NetworkInformation
ACE Capability areas
Potential bank segments and product areas

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THE “BILINGUAL”
• Brief profile ofa “bilingual”VP/AVP
• Has high business acumen andcan have atop-down and
bottom up view ofthe problem
• Acts as the project manager andis delivery focused
• Builds trusted relationships quickly across multiple
functions and levels
• Has high emotional intelligence andpeople skills
• Canclearly communicate complex subjects
• Has consulting approach to problems
Indicative skills andexperiences
• At least one area ofexpertise (CVM,Pricing, Information products)
• SQL,SAS,Excel, P&L knowledge

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THE “RENAISSANCE”
PERSON
• The “data scientist” evolution of the “bilingual”
• Has extraordinary curiosity and enjoys crunching
(unstructured) data
• Is statistically savvy, usually a PhD in Statistics,
Computer Science, Behavioural Sciences or
Physics
• Can apply different concepts and techniques to
other fields
• Understands the fundamentals of machine
learning, data infrastructure
• Has entrepreneurial attitude towards trivial
problems
Indicative skills andexperiences
• Usually worked in major tech orstart-up previously as aprogrammer, statistician etc
• Scala, Python,Hadoop, Java

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THE CAPABILITY EXPERT
• Typically a fantastic modeller or subject matter expert in a
portfolioorfunctional area
• Has in-depth experience across multiple sectors andcan
be a trusted advisor forthe bilingual project leadas well
• Embodies the excellence in a functional area
Indicative skills andexperiences
• Multisector experience in one of pricing,marketing analytics orsector expertise
• SQL,SAS,Excel, P&L knowledge

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THE DATA “NINJA”
• Has deep knowledge of the bank data
systems and architecture
• Has been around long enough to be well
connected across remote areas
• Can usually tell you which database table
and column contains what you need
without a dictionary
Indicative skills andexperiences
• n-depth data analysis and profiling,can code super-efficient processes in their respective expertise area
• SQL,SAS

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RUNNING A COE AS A
CONSULTING TEAM
• Projects are being resourced on an
incremental value basis
• Council Heads are the “Partners”,
responsible for high stakeholder
engagement
• Council leads are effectively the “account
engagement managers” as well as project
delivery consultants for up to 7-8
initiatives
• Senior AVP/VP are project leads working
with the rest of the team on 1-2 projects
simultaneously
• We can to cross-train analysts in different
capability areas via projects
• Each one is expected to write code,
presentations, project manage, problem
solve, run workshops and provide
leadership whenever needed
Product or segment Head aka ‘Council’
Council Head
(MD/Director)
Council Lead
(VP)
Decision Analyst
(AVP)
Senior Decision
Analyst
(VP)
Project Lead
(AVP/VP)
Decision Analyst
(AVP)
Project Lead
(AVP/VP)
Example client team
Example Client
Pool of (Senior) Decision Analysts
Staff rotation
How does it work

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WHO WOULD YOU WANT TO
BE
Wants
Ability to
win
Future
changes
Adding
value
…don’tthinktwice
Bilingual
Data “ninja”
The“renessance”scientist
Capabilityexpert

23. TEA TIME
FOR THOSE THAT HAD ENOUGH

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TIME TO EARN YOUR LUNCH:
CASE STUDY 1 (30MINS)
You have just joined the bank.
The MD of the Analytics MD wishes to hear your thoughts about
how she could develop a world-class information product targeted
to Small-Medium Businesses. It’s not clear what she has in mind.
How would you approach such a problem statement?

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A SIMPLE FRAMEWORK
Background
You have just joined the bank.
The MD of Analytics wishes to
hear your thoughts about
how she could develop a
world-class information
product targeted to Small-
Medium Businesses. It’s not
clear what she has in mind.
Question
How would you develop this
proposition?
Develop a
information
proposition for
small business
What could be
your offering
Own data assets
What need would
you be able to
serve
Enablers
Organisational
structure
Analytical and tech
capabilities
Legal/Compliance
Market Size &
Growth
Target sectors
Market size
Growth
Who would be
your competitors
Price (what would
be your business
model)
Go to market
Partners
Existing channels

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WHAT’S UNIQUE ABOUT
YOUR OFFERING
business services
Consumer retail
banking
Corporate &
Small business
banking
Investment
Banking
Consumer
services
20-35% share
(UK) in each
sector
Top 10 bank
worldwide
1st -2nd bank
across most
products in UK
Good innovation
history

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POTENTIAL TARGET
SECTORS

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PARTNERING OR GOING
SOLO
Source: Oliver Wyman, “winningthe digitalbattlefield”

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Front end
• Growth in data
traffice
• Multichannel data
flows
Middleware
• Loyalty programmes,
dynamic pricing,
online communities
• Real time processing
• Data management
Back end
• Resilient, fast,
transacting, booking
and accounting
systems
WOULD YOU STAY
CONNECTED?
Source: AT Kearney,“FSin the age of digital”, (adapted)
Branch Web/Mobile
Third-party
apps
Customer intelligence kernel
Customer intelligence kernel
Value added functionalities
API API API

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TIME TO EARN YOUR FRIDAY DRINKS: CASE
STUDY 2 (30MINS)
You are the MD of the Direct Investing plattform
For the past 2 years, you have been building platform to provide
execution only investment services. You believe this would work
brilliantly and you bet your career on it. The analytics team has
worked out the pricing and looks like you’ll be making a good
deal of money for the next three years.
All you have to do is sit back and enjoy the ride. The product will
take care of itself. Or maybe not.
How do you make sure your venture doesn’t turn into a flop?

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A SIMPLE FRAMEWORK
Improve business unit
profitability
Acquire new
customers
Where
When
How
Improve existing
customer profitability
Price higher
Deepen relationship
Improve experience
Improve product
Reduce attrition
Reduce running costs
How do youmakesure yourventure
doesn’t turn into aflop?
Yourgoalis tofindwaysto utilisethe
datayouhave

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WE’VE ONLY PROCESSED ABOUT 0.5% OF ALL
THE AVAILABLE DATA
3.1m oil barrelsc80bnrecoverable barrels
We’ve extractedjust110oil tankers
Outof27,000oil tankerstill it dries

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WE GENERATE EVEN MORE
EVERY YEAR
0.8 ZB 1.2 ZB
7.9 ZB
35.0 ZB
-
1,000
2,000
3,000
4,000
5,000
6,000
2008 2010 2012 2014 2016 2018 2020 2022 2024
Growth(indexto2009)
Year
Annual data generation globally
44x more
since2009
Mostofitwouldbeyourdata,
managedbyenterprises
What is a zettabyte
Equivalent in
Zettabytes
1 gigabyte 1,000,000,000,000
1 terabyte 1,000,000,000
1 petabyte 1,000,000
1 exabyte 1,000
1 zettabyte 1

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WHAT COULD THIS MEAN
FOR YOU