The document discusses the evolution and significance of big data in credit card management, highlighting Barclaycard's multi-pronged approach over the last 5 years to utilize data-driven tools for better customer interactions. It emphasizes the need for a seamless customer experience and adaptability in the face of changing market dynamics and regulatory pressures. Ultimately, it envisions Barclaycard as a 'go-to' bank through enhanced products and services tailored to customers' needs.

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