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Big Data? Big Deal
Mark T. Warren
Director of Decision Science
Barclaycard
A starting point
• Credit Card scoring blazes the trail for Big Data
A starting point
• Credit Card scoring blazes the trail for Big Data
 Risk scoring dates to the early sixties
 Account management scoring to the early eighties
 Direct mail spurs the next wave of innovation/development
A starting point
• Credit Card scoring blazes the trail for Big Data
 Risk scoring dates to the early sixties
 Account management scoring to the early eighties
 Direct mail spurs the next wave of innovation/development
• Virtually every customer touch point is highly dependent on statistical models
imbedded in near real time systems fed by a wide variety of data
• The existence of such tools … and the proper use of them by credit managers … is
the foundation of credit card management today.
A starting point
• Credit Card scoring blazes the trail for Big Data
 Risk scoring dates to the early sixties
 Account management scoring to the early eighties
 Direct mail spurs the next wave of innovation/development
• Virtually every customer touch point is highly dependent on statistical models
imbedded in near real time systems fed by a wide variety of data
• The existence of such tools … and the proper use of them by credit managers … is
the foundation of credit card management today.
• So … Big Data? Big Deal
ABSA
Cards
Merchant Acquiring
Entercard JV
Cards
US
Cards
UK
Cards
Sales Finance
Merchant Acquiring
Germany
Cards
Instalment Loans
Revolving Loans
Spain, Portugal
& Italy
Cards
Barclaycard Footprint
Edcon JV
Cards
Private Label
WFS JV
Cards
Loans
Private Label
ABSA
Cards
Merchant Acquiring
Entercard JV
Cards
US
Cards
UK
Cards
Sales Finance
Merchant Acquiring
Germany
Cards
Instalment Loans
Revolving Loans
Spain, Portugal
& Italy
Cards
Barclaycard Footprint
Edcon JV
Cards
Private Label
WFS JV
Cards
Loans
Private Label
Barclaycard overall (in round numbers)
 30M Accounts worldwide
 50M Transactions/Month
 1.5M Inbound Customer Calls/Month
 .5M Outbound Calls/Month
 10M UK Web Logins/Month
Building the Foundation – The Goal
• For the past 5 years Barclaycard has pursued a multi-prong approach aimed at
rolling out best-in-class tools that rely on a broad array of data and are embedded in
internally managed systems.
Building the Foundation – The Goal
• For the past 5 years Barclaycard has pursued a multi-prong approach aimed at
rolling out best-in-class tools that rely on a broad array of data and are embedded in
internally managed systems.
 Scalable
o Central teams supporting geographically dispersed portfolios
 Common toolset
o Development tools for analysts
o Scores/models for the business
 Integrated
o Card data, retail data, and bureau data give a full view of the customer
o Common platform for risk and marketing purposes
Building the Foundation – The Goal
• For the past 5 years Barclaycard has pursued a multi-prong approach aimed at
rolling out best-in-class tools that rely on a broad array of data and are embedded in
internally managed systems.
 Scalable
o Central teams supporting geographically dispersed portfolios
 Common toolset
o Development tools for analysts
o Scores/models for the business
 Integrated
o Card data, retail data, and bureau data give a full view of the customer
o Common platform for risk and marketing purposes
• Today, Barclaycard deploys 200+ predictive scores across its portfolios to manage
touch points throughout the customer life-cycle.
Building the Foundation – The Goal
• For the past 5 years Barclaycard has pursued a multi-prong approach aimed at
rolling out best-in-class tools that rely on a broad array of data and are embedded in
internally managed systems.
 Scalable
o Central teams supporting geographically dispersed portfolios
 Common toolset
o Development tools for analysts
o Scores/models for the business
 Integrated
o Card data, retail data, and bureau data give a full view of the customer
o Common platform for risk and marketing purposes
• Today, Barclaycard deploys 200+ predictive scores across its portfolios to manage
touch points throughout the customer life-cycle.
Le t’s take a q uick lo o k at ECMsco ring platfo rm s – the o rig inalbig data so lutio n
“Black Box”
Processing Engine
Data management
Score calculations
Decision support
Output
Raw data
Data
Pre-process
Information Scoring Touch-points
Authorization
Module
CLI
Module
Collections
Module
Action
Action
Action
Card
Masterfile
Credit Bureau
Retail
Masterfile?
Extra?
Authorization
Collections
Partner
Third Party
Collections
Customer Service
Building the Foundation – an example
Data Processed
 350G – 450G
“Black Box”
Processing Engine
Data management
Score calculations
Decision support
Output
Raw data
Data
Pre-process
Information Scoring Touch-points
Authorization
Module
CLI
Module
Collections
Module
Action
Action
Action
Card
Masterfile
Credit Bureau
Retail
Masterfile?
Extra?
Authorization
Collections
Partner
Third Party
Collections
Customer Service
Building the Foundation – an example
Daily Run Time
 5-10 hours
“Black Box”
Processing Engine
Data management
Score calculations
Decision support
Output
Raw data
Data
Pre-process
Information Scoring Touch-points
Authorization
Module
CLI
Module
Collections
Module
Action
Action
Action
Card
Masterfile
Credit Bureau
Retail
Masterfile?
Extra?
Authorization
Collections
Partner
Third Party
Collections
Customer Service
Building the Foundation – an example
Scale
 8-10M Customers
 Up to 20 scores
Building the Foundation -- Learnings
1. Common operational platforms are key
 Without them you can’t get scale
Building the Foundation -- Learnings
1. Common operational platforms are key
 Without them you can’t get scale
2. Critical role of flexible analytic architecture
 Not just a technical capability but a software and licensing capability
Building the Foundation -- Learnings
1. Common operational platforms are key
 Without them you can’t get scale
2. Critical role of flexible analytic architecture
 Not just a technical capability but a software and licensing capability
3. Addressing Data Privacy concerns while making data available to analysts
 EU and US regulatory regimes unique and restrictive
Building the Foundation -- Learnings
1. Common operational platforms are key
 Without them you can’t get scale
2. Critical role of flexible analytic architecture
 Not just a technical capability but a software and licensing capability
3. Addressing Data Privacy concerns while making data available to analysts
 EU and US regulatory regimes unique and restrictive
4. Quality models depend on market understanding
 Since results must be interpretable, context is everything
Building the Foundation -- Learnings
1. Common operational platforms are key
 Without them you can’t get scale
2. Critical role of flexible analytic architecture
 Not just a technical capability but a software and licensing capability
3. Addressing Data Privacy concerns while making data available to analysts
 EU and US regulatory regimes unique and restrictive
4. Quality models depend on market understanding
 Since results must be interpretable, context is everything
5. Data mining has its pitfalls
 Numbers do lie or,
 Blindly following numbers yields poor customer experience
Market Change
• But our industry is changing
Market Change
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
Market Change
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
 Increased regulatory oversight
o Increased scrutiny often requiring quick turn around time
Market Change
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
 Increased regulatory oversight
o Increased scrutiny often requiring quick turn around time
 Reduced margins
o Revenue streams such as fees are increasingly limited
Market Change
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
 Increased regulatory oversight
o Increased scrutiny often requiring quick turn around time
 Reduced margins
o Revenue streams such as fees are increasingly limited
 Changing customer behaviour
o Reduced appetite for debt and increased demand for quality
• These trends aren’t unique to the US nor are they unique to credit cards
Market Change
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
 Increased regulatory oversight
o Increased scrutiny often requiring quick turn around time
 Reduced margins
o Revenue streams such as fees are increasingly limited
 Changing customer behaviour
o Reduced appetite for debt and increased demand for quality
• These trends aren’t unique to the US nor are they unique to credit cards
So our goal is to be the ‘Go-To’ bank
• But our industry is changing
 New competitors (PayPal, etc.)
o PayPal, etc. utilize newer platforms to provide unique services
 Increased regulatory oversight
o Increased scrutiny often requiring quick turn around time
 Reduced margins
o Revenue streams such as fees are increasingly limited
 Changing customer behaviour
o Reduced appetite for debt and increased demand for quality
• These trends aren’t unique to the US nor are they unique to credit cards
So our goal is to be the ‘Go-To’ bank
• In short … if people want to be our customers we’ll have a long-term viable business
model
Market Change
Can Big Data help us become the ‘Go-To’ bank?
Big Data solutions are often sold on the following merits:
•Reduced costs
 Disk, Processing, Back-up
 Open source software
•Faster analytics
 MPP/IMP
 Real-time/Near Real-time processing
Can Big Data help us become the ‘Go-To’ bank?
Big Data solutions are often sold on the following merits:
•Reduced costs
 Disk, Processing, Back-up
 Open source software
•Faster analytics
 MPP/IMP
 Real-time/Near Real-time processing
While these savings can be significant there is one simple obstacle …
… we’ve already made significant investments in such technology.
Our costs are already sunk – adopting newer platforms is an incremental cost
Can Big Data help us become the ‘Go-To’ bank?
• Getting people to want to be our customers takes way more than keeping our losses
in check
• We need to have a more complete view of the customer
 Are we making their lives easy when they use our product?
 Are we meeting their needs in a responsible way?
 Are we adding to their lives by providing products and services that go beyond
commodity features?
• Getting people to want to be our customers takes way more than keeping our losses
in check
• We need to have a more complete view of the customer
 Are we making their lives easy when they use our product?
 Are we meeting their needs in a responsible way?
 Are we adding to their lives by providing products and services that go beyond
commodity features?
• Bureau data, card usage data, and payment data doesn’t give us much insight into
these questions.
So Big Data is not just about adding additional X’s to the mix …
…. It is about creating new Y’s to investigate
Can Big Data help us become the ‘Go-To’ bank?
First steps …
2013 Focuses on Proof-of-Concept initiatives:
 Hadoop tests (US)
 SAS High Power Analytic tests (UK)
 Voice of the Customer initiatives using Verint speech-to-text analytics
 Customer specific web presentment (UK)
2014 takes these learnings and deploys new solutions
… Next steps …
The next 3 years entails:
 New data (of course)
o Web logs
o Customer calls
o AID transaction data
 New hardware and software to house this data
o Globally available analytic environments where cost isn’t an issue in investigating data
 New skills
o Deriving information from unstructured data
o Investigating alternative modelling techniques where feasible
… Next steps …
The next 3 years entails:
 New data (of course)
o Web logs
o Customer calls
o AID transaction data
 New hardware and software to house this data
o Globally available analytic environments where cost isn’t an issue in investigating data
 New skills
o Deriving information from unstructured data
o Investigating alternative modelling techniques where feasible
Key challenges:
 Market understanding increasingly critical
o Cultural norms more pronounced in unstructured data
 Increased complexity of implementations
o Timeliness of results increasingly critical
o Accessing a wide variety of contextual data as customers use our products
... Pivotal change …
• Whereas data intensive statistical analytics has been the mainstay of Risk
Management and Marketing, Big Data opens the door to driving Operations and new
business lines.
• The beauty of this is the following:
 Whereas the business case for replacing existing hardware and software that drives
today’s analytics is often weak, the Big Data business case thrives in operations.
 Tackling new areas requires new investment.
 With that new hardware/software in place, it is then feasible to migrate existing
traditional analytics to that new platform.
... Pivotal change …
• Whereas data intensive statistical analytics has been the mainstay of Risk
Management and Marketing, Big Data opens the door to driving Operations and new
business lines.
• The beauty of this is the following:
 Whereas the business case for replacing existing hardware and software that drives
today’s analytics is often weak, the Big Data business case thrives in operations.
 Tackling new areas requires new investment.
 With that new hardware/software in place, it is then feasible to migrate existing
traditional analytics to that new platform.
So Big Data is a Big De al
… the Destination
• So what does the ‘Go-To’ bank look like in 3 years for Barclays?
 Seamless customer service
… the Destination
• So what does the ‘Go-To’ bank look like in 3 years for Barclays?
 Seamless customer service
 Products work for the unique needs of our customers
… the Destination
• So what does the ‘Go-To’ bank look like in 3 years for Barclays?
 Seamless customer service
 Products work for the unique needs of our customers
 Unique enhancements suited to each customer’s wishes
… the Destination
• So what does the ‘Go-To’ bank look like in 3 years for Barclays?
 Seamless customer service
 Products work for the unique needs of our customers
 Unique enhancements suited to each customer’s wishes
 Stronger financial position for Barclays given significantly reduced costs

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Big Data? Big Deal, Barclaycard

  • 1. Big Data? Big Deal Mark T. Warren Director of Decision Science Barclaycard
  • 2. A starting point • Credit Card scoring blazes the trail for Big Data
  • 3. A starting point • Credit Card scoring blazes the trail for Big Data  Risk scoring dates to the early sixties  Account management scoring to the early eighties  Direct mail spurs the next wave of innovation/development
  • 4. A starting point • Credit Card scoring blazes the trail for Big Data  Risk scoring dates to the early sixties  Account management scoring to the early eighties  Direct mail spurs the next wave of innovation/development • Virtually every customer touch point is highly dependent on statistical models imbedded in near real time systems fed by a wide variety of data • The existence of such tools … and the proper use of them by credit managers … is the foundation of credit card management today.
  • 5. A starting point • Credit Card scoring blazes the trail for Big Data  Risk scoring dates to the early sixties  Account management scoring to the early eighties  Direct mail spurs the next wave of innovation/development • Virtually every customer touch point is highly dependent on statistical models imbedded in near real time systems fed by a wide variety of data • The existence of such tools … and the proper use of them by credit managers … is the foundation of credit card management today. • So … Big Data? Big Deal
  • 6. ABSA Cards Merchant Acquiring Entercard JV Cards US Cards UK Cards Sales Finance Merchant Acquiring Germany Cards Instalment Loans Revolving Loans Spain, Portugal & Italy Cards Barclaycard Footprint Edcon JV Cards Private Label WFS JV Cards Loans Private Label
  • 7. ABSA Cards Merchant Acquiring Entercard JV Cards US Cards UK Cards Sales Finance Merchant Acquiring Germany Cards Instalment Loans Revolving Loans Spain, Portugal & Italy Cards Barclaycard Footprint Edcon JV Cards Private Label WFS JV Cards Loans Private Label Barclaycard overall (in round numbers)  30M Accounts worldwide  50M Transactions/Month  1.5M Inbound Customer Calls/Month  .5M Outbound Calls/Month  10M UK Web Logins/Month
  • 8. Building the Foundation – The Goal • For the past 5 years Barclaycard has pursued a multi-prong approach aimed at rolling out best-in-class tools that rely on a broad array of data and are embedded in internally managed systems.
  • 9. Building the Foundation – The Goal • For the past 5 years Barclaycard has pursued a multi-prong approach aimed at rolling out best-in-class tools that rely on a broad array of data and are embedded in internally managed systems.  Scalable o Central teams supporting geographically dispersed portfolios  Common toolset o Development tools for analysts o Scores/models for the business  Integrated o Card data, retail data, and bureau data give a full view of the customer o Common platform for risk and marketing purposes
  • 10. Building the Foundation – The Goal • For the past 5 years Barclaycard has pursued a multi-prong approach aimed at rolling out best-in-class tools that rely on a broad array of data and are embedded in internally managed systems.  Scalable o Central teams supporting geographically dispersed portfolios  Common toolset o Development tools for analysts o Scores/models for the business  Integrated o Card data, retail data, and bureau data give a full view of the customer o Common platform for risk and marketing purposes • Today, Barclaycard deploys 200+ predictive scores across its portfolios to manage touch points throughout the customer life-cycle.
  • 11. Building the Foundation – The Goal • For the past 5 years Barclaycard has pursued a multi-prong approach aimed at rolling out best-in-class tools that rely on a broad array of data and are embedded in internally managed systems.  Scalable o Central teams supporting geographically dispersed portfolios  Common toolset o Development tools for analysts o Scores/models for the business  Integrated o Card data, retail data, and bureau data give a full view of the customer o Common platform for risk and marketing purposes • Today, Barclaycard deploys 200+ predictive scores across its portfolios to manage touch points throughout the customer life-cycle. Le t’s take a q uick lo o k at ECMsco ring platfo rm s – the o rig inalbig data so lutio n
  • 12. “Black Box” Processing Engine Data management Score calculations Decision support Output Raw data Data Pre-process Information Scoring Touch-points Authorization Module CLI Module Collections Module Action Action Action Card Masterfile Credit Bureau Retail Masterfile? Extra? Authorization Collections Partner Third Party Collections Customer Service Building the Foundation – an example Data Processed  350G – 450G
  • 13. “Black Box” Processing Engine Data management Score calculations Decision support Output Raw data Data Pre-process Information Scoring Touch-points Authorization Module CLI Module Collections Module Action Action Action Card Masterfile Credit Bureau Retail Masterfile? Extra? Authorization Collections Partner Third Party Collections Customer Service Building the Foundation – an example Daily Run Time  5-10 hours
  • 14. “Black Box” Processing Engine Data management Score calculations Decision support Output Raw data Data Pre-process Information Scoring Touch-points Authorization Module CLI Module Collections Module Action Action Action Card Masterfile Credit Bureau Retail Masterfile? Extra? Authorization Collections Partner Third Party Collections Customer Service Building the Foundation – an example Scale  8-10M Customers  Up to 20 scores
  • 15. Building the Foundation -- Learnings 1. Common operational platforms are key  Without them you can’t get scale
  • 16. Building the Foundation -- Learnings 1. Common operational platforms are key  Without them you can’t get scale 2. Critical role of flexible analytic architecture  Not just a technical capability but a software and licensing capability
  • 17. Building the Foundation -- Learnings 1. Common operational platforms are key  Without them you can’t get scale 2. Critical role of flexible analytic architecture  Not just a technical capability but a software and licensing capability 3. Addressing Data Privacy concerns while making data available to analysts  EU and US regulatory regimes unique and restrictive
  • 18. Building the Foundation -- Learnings 1. Common operational platforms are key  Without them you can’t get scale 2. Critical role of flexible analytic architecture  Not just a technical capability but a software and licensing capability 3. Addressing Data Privacy concerns while making data available to analysts  EU and US regulatory regimes unique and restrictive 4. Quality models depend on market understanding  Since results must be interpretable, context is everything
  • 19. Building the Foundation -- Learnings 1. Common operational platforms are key  Without them you can’t get scale 2. Critical role of flexible analytic architecture  Not just a technical capability but a software and licensing capability 3. Addressing Data Privacy concerns while making data available to analysts  EU and US regulatory regimes unique and restrictive 4. Quality models depend on market understanding  Since results must be interpretable, context is everything 5. Data mining has its pitfalls  Numbers do lie or,  Blindly following numbers yields poor customer experience
  • 20. Market Change • But our industry is changing
  • 21. Market Change • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services
  • 22. Market Change • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services  Increased regulatory oversight o Increased scrutiny often requiring quick turn around time
  • 23. Market Change • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services  Increased regulatory oversight o Increased scrutiny often requiring quick turn around time  Reduced margins o Revenue streams such as fees are increasingly limited
  • 24. Market Change • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services  Increased regulatory oversight o Increased scrutiny often requiring quick turn around time  Reduced margins o Revenue streams such as fees are increasingly limited  Changing customer behaviour o Reduced appetite for debt and increased demand for quality • These trends aren’t unique to the US nor are they unique to credit cards
  • 25. Market Change • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services  Increased regulatory oversight o Increased scrutiny often requiring quick turn around time  Reduced margins o Revenue streams such as fees are increasingly limited  Changing customer behaviour o Reduced appetite for debt and increased demand for quality • These trends aren’t unique to the US nor are they unique to credit cards So our goal is to be the ‘Go-To’ bank
  • 26. • But our industry is changing  New competitors (PayPal, etc.) o PayPal, etc. utilize newer platforms to provide unique services  Increased regulatory oversight o Increased scrutiny often requiring quick turn around time  Reduced margins o Revenue streams such as fees are increasingly limited  Changing customer behaviour o Reduced appetite for debt and increased demand for quality • These trends aren’t unique to the US nor are they unique to credit cards So our goal is to be the ‘Go-To’ bank • In short … if people want to be our customers we’ll have a long-term viable business model Market Change
  • 27. Can Big Data help us become the ‘Go-To’ bank? Big Data solutions are often sold on the following merits: •Reduced costs  Disk, Processing, Back-up  Open source software •Faster analytics  MPP/IMP  Real-time/Near Real-time processing
  • 28. Can Big Data help us become the ‘Go-To’ bank? Big Data solutions are often sold on the following merits: •Reduced costs  Disk, Processing, Back-up  Open source software •Faster analytics  MPP/IMP  Real-time/Near Real-time processing While these savings can be significant there is one simple obstacle … … we’ve already made significant investments in such technology. Our costs are already sunk – adopting newer platforms is an incremental cost
  • 29. Can Big Data help us become the ‘Go-To’ bank? • Getting people to want to be our customers takes way more than keeping our losses in check • We need to have a more complete view of the customer  Are we making their lives easy when they use our product?  Are we meeting their needs in a responsible way?  Are we adding to their lives by providing products and services that go beyond commodity features?
  • 30. • Getting people to want to be our customers takes way more than keeping our losses in check • We need to have a more complete view of the customer  Are we making their lives easy when they use our product?  Are we meeting their needs in a responsible way?  Are we adding to their lives by providing products and services that go beyond commodity features? • Bureau data, card usage data, and payment data doesn’t give us much insight into these questions. So Big Data is not just about adding additional X’s to the mix … …. It is about creating new Y’s to investigate Can Big Data help us become the ‘Go-To’ bank?
  • 31. First steps … 2013 Focuses on Proof-of-Concept initiatives:  Hadoop tests (US)  SAS High Power Analytic tests (UK)  Voice of the Customer initiatives using Verint speech-to-text analytics  Customer specific web presentment (UK) 2014 takes these learnings and deploys new solutions
  • 32. … Next steps … The next 3 years entails:  New data (of course) o Web logs o Customer calls o AID transaction data  New hardware and software to house this data o Globally available analytic environments where cost isn’t an issue in investigating data  New skills o Deriving information from unstructured data o Investigating alternative modelling techniques where feasible
  • 33. … Next steps … The next 3 years entails:  New data (of course) o Web logs o Customer calls o AID transaction data  New hardware and software to house this data o Globally available analytic environments where cost isn’t an issue in investigating data  New skills o Deriving information from unstructured data o Investigating alternative modelling techniques where feasible Key challenges:  Market understanding increasingly critical o Cultural norms more pronounced in unstructured data  Increased complexity of implementations o Timeliness of results increasingly critical o Accessing a wide variety of contextual data as customers use our products
  • 34. ... Pivotal change … • Whereas data intensive statistical analytics has been the mainstay of Risk Management and Marketing, Big Data opens the door to driving Operations and new business lines. • The beauty of this is the following:  Whereas the business case for replacing existing hardware and software that drives today’s analytics is often weak, the Big Data business case thrives in operations.  Tackling new areas requires new investment.  With that new hardware/software in place, it is then feasible to migrate existing traditional analytics to that new platform.
  • 35. ... Pivotal change … • Whereas data intensive statistical analytics has been the mainstay of Risk Management and Marketing, Big Data opens the door to driving Operations and new business lines. • The beauty of this is the following:  Whereas the business case for replacing existing hardware and software that drives today’s analytics is often weak, the Big Data business case thrives in operations.  Tackling new areas requires new investment.  With that new hardware/software in place, it is then feasible to migrate existing traditional analytics to that new platform. So Big Data is a Big De al
  • 36. … the Destination • So what does the ‘Go-To’ bank look like in 3 years for Barclays?  Seamless customer service
  • 37. … the Destination • So what does the ‘Go-To’ bank look like in 3 years for Barclays?  Seamless customer service  Products work for the unique needs of our customers
  • 38. … the Destination • So what does the ‘Go-To’ bank look like in 3 years for Barclays?  Seamless customer service  Products work for the unique needs of our customers  Unique enhancements suited to each customer’s wishes
  • 39. … the Destination • So what does the ‘Go-To’ bank look like in 3 years for Barclays?  Seamless customer service  Products work for the unique needs of our customers  Unique enhancements suited to each customer’s wishes  Stronger financial position for Barclays given significantly reduced costs