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