big data? big deal, barclaycard
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Big Data? Big Deal
Mark T. WarrenDirector 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
ABSACardsMerchant Acquiring
Entercard JVCards
USCards
UKCardsSales FinanceMerchant Acquiring
GermanyCardsInstalment LoansRevolving Loans
Spain, Portugal& ItalyCards
Barclaycard Footprint
Edcon JVCardsPrivate Label
WFS JVCardsLoansPrivate Label
ABSACardsMerchant Acquiring
Entercard JVCards
USCards
UKCardsSales FinanceMerchant Acquiring
GermanyCardsInstalment LoansRevolving Loans
Spain, Portugal& ItalyCards
Barclaycard Footprint
Edcon JVCardsPrivate Label
WFS JVCardsLoansPrivate 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.
Scalableo Central teams supporting geographically dispersed portfolios
Common toolset o Development tools for analystso 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.
Scalableo Central teams supporting geographically dispersed portfolios
Common toolset o Development tools for analystso 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.
Scalableo Central teams supporting geographically dispersed portfolios
Common toolset o Development tools for analystso 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 ta ke a q uic k lo o k a t ECM s c o ring p la tfo rm s – the o rig ina l big da ta s o lutio n
“Black Box” Processing Engine
Data managementScore calculationsDecision support
Output
Raw data
Data Pre-process
Information Scoring Touch-points
AuthorizationModule
CLIModule
CollectionsModule
Action
Action
Action
Card Masterfile
Credit Bureau
RetailMasterfile?
Extra?
Authorization Collections
Partner Third Party
Collections
Customer Service
Building the Foundation – an example
Data Processed 350G – 450G
“Black Box” Processing Engine
Data managementScore calculationsDecision support
Output
Raw data
Data Pre-process
Information Scoring Touch-points
AuthorizationModule
CLIModule
CollectionsModule
Action
Action
Action
Card Masterfile
Credit Bureau
RetailMasterfile?
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 managementScore calculationsDecision support
Output
Raw data
Data Pre-process
Information Scoring Touch-points
AuthorizationModule
CLIModule
CollectionsModule
Action
Action
Action
Card Masterfile
Credit Bureau
RetailMasterfile?
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 oversighto 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 oversighto Increased scrutiny often requiring quick turn around time
Reduced marginso 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 oversighto Increased scrutiny often requiring quick turn around time
Reduced marginso Revenue streams such as fees are increasingly limited
Changing customer behaviouro 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 oversighto Increased scrutiny often requiring quick turn around time
Reduced marginso Revenue streams such as fees are increasingly limited
Changing customer behaviouro 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 oversighto Increased scrutiny often requiring quick turn around time
Reduced marginso Revenue streams such as fees are increasingly limited
Changing customer behaviouro 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 logso Customer callso AID transaction data
New hardware and software to house this datao Globally available analytic environments where cost isn’t an issue in investigating data
New skillso Deriving information from unstructured datao Investigating alternative modelling techniques where feasible
… Next steps …The next 3 years entails:
New data (of course)o Web logso Customer callso AID transaction data
New hardware and software to house this datao Globally available analytic environments where cost isn’t an issue in investigating data
New skillso Deriving information from unstructured datao 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 criticalo 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 Da ta is a Big De a l
… 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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