How to overcome Big Data challenges...–Batch production ... 18 ©Experian 4/15/2017 Experian Public Vision 2017 Use Case 2 Targeting top of wallet off – Bank ABC spend Metrics

How to overcome Big Data challenges

Introducing:

Satya Lakkaraju Citi

Bryon Evans Experian

Natasha Madan Experian

©Experian 3 4/15/2017 Experian Public Vision 2017

1. Environment

2. Demonstration

3. Analytics on Demand

4. Q&A with Citi

Contents


Experian’s Big Data platform


What are some perceptions of Big Data?


Experian’s Big Data platform enables integrated analysis, development, and deployment of focused solutions for our clients

• The initial environment consists of:

– Client analytic environment

• Shared or dedicated

– Batch production

• Prescreen, account review

– TIP updated multiple times per day

• Client “production” environment (PDB)

• Archives

What is Experian’s Big Data platform Enabling a wide set of capabilities


An environment that provides clients unprecedented access to Experian’s consumer data

• Fifteen+ years of month-end 100% depersonalized consumer data

– Data covering over 300 million consumers per month

– Industry leading scores, attributes

• VantageScore® 3.0

• Premier Attributes℠

• Trended Attributes

Analytical environment Access to best in class attributes, scores and trended attributes

No costly infrastructure builds

or IT projects needed to use


An environment that provides a comprehensive data and tools to enable clients to create scores, attributes and decisions

• Trade, inquiry, public record, trended, and extended data

– Over 1.1 trillion rows of TIP data for big data analytics

• Best-in-class software

– R

– H20

– Python

– Anaconda

Analytical environment Data and tools to develop insights


• Easily create, validate and deploy attributes and strategies using Experian tools

• Attribute Toolbox™ (ATB) and PowerCurve® Strategy Management

– Develop and validate attributes in the Experian analytical environment

– Easily deploy in our File OneSM environment

• Ability to deploy in batch coming soon

Analytical environment Development and deployment


Analytical environment demo


“Information is the oil of the 21st century, and analytics is the combustion engine.”

— Peter Sondergaard Senior Vice President

Gartner Research


Analytics on demand


Get more, do more

Key topic Description Approach

Score migration

Gauge movement of key score averages and relationship to performance with industry comparisons

• Calculate VantageScore® migration for client portfolio and overall industry

New account vs. existing accounts

How do balances on/off client differ for those that migrate 20+ points?

Benchmarking Develop key portfolio stats

• Coded for generic benchmark to industry

• Evaluate correlation between lines of business (auto, mortgage, card, etc.)

• Perform validations on portfolio slices by geography, industries, product types

Market entry Conduct prospect development review and analyses

• Identify opportunities between lines of business (auto, mortgage, card, etc.)

• Evaluation of new vs. existing accounts, current vs. derogatory, etc.

• Create peer grouping

Wallet share Assess customer relationships with details about their overall spend

• Share of balance and credit limit (basic)

• Consumer spend available through Total Annual Plastic Spend (Experian TAPSSM) and Estimated Interest Rate Calculator (EIRCSM)

• Trade level spend and EIRC allow for more detailed on-them/off-them spend comparison

Incremental share Compare portfolio usage pre- and post-acquisition

• Due diligence for future acquisitions and post-acquisition performance

• Portfolio reference files flag portfolios/trades


Reject bias

• Bias created by a creditor’s approach to risk management

• Results in an incomplete picture of the through the door population

• High decline rates with very consistent decision making result in the most problematic bias

Use case 1 Reject inference

Total Applications

Approvals Declines

Not Booked Unknown

Good Unknown

Bad Booked

Declines

Unknown Good

Unknown Bad


Use case 1 Reject inference

“If this applicant had been accepted,

how would they have performed?” Bureau inference

Step 5

Step 5

Identify goods

vs. bads and re-

adjust policies

5

Step 4

Track loan

performance of

the new loan 4

Step 4

Step 3

Identify consumers

tagged in step 2; who

open a similar product

loan within 3-6 months

3

Step 3

Step 2

Experian tags

those applicants in

the big data

environment 2

Step 2

Step 1

Client sends

applicant file

of declines.

1

Step 1


Use Case 2 Revenue potential

“How much share

did I capture?”

“Identify

opportunities”

“How profitable are

the consumers?”


Use Case 2 Wallet share analysis

The Experian Profit Suite

is integral in understanding

the share of wallet for your

consumer base.

Metrics Bank ABC bankcard Share of wallet

VantageScore 3.0 ® 659

On

-bo

ok

Average # of trades 1.2 19.5%

Average credit line $3,813 15.6%

Average revolving balance $1,107 20.7%

Average annual spend $2,745 21.3%

Average effective yield 25.58%

Annual interest $317 30.0%

Estimated interchange $46 21.3%

Off

-bo

ok

Average # of trades 4.96

Average credit line $20,663

Average revolving balance $4,233

Average annual spend $10,167

Average effective yield 4.93%

Annual interest $739

Estimated interchange $170

Experian TAPSSM and EIRCSM data elements


Use Case 2 Targeting top of wallet off – Bank ABC spend

Metrics

Consumer top

of wallet trade

Bank ABC (48%) Other (52%)

Ris

k

Average VantageScore® 661 646 670

Delinquency rate (60+ DPD) 1.9% 2.9% 1.3%

Us

e

Average credit line $5,712 $4,044 $6,772

Average revolving balance $1,644 $1,022 $2,040

Avg revolving utilization 29% 25% 30%

Annual spend $7,195 $4,126 $8,877

Reven

ue

Effective yield 18.89% 22.46% 16.89%

Annual interest $456 $333 $527

Estimated interchange $120 $69 $148

Opportunity for increased revenue exists for those existing

customers where the client is not top of wallet …

Which customers should be targeted?


Use Case 2 Segmenting the highest spend cards (off Bank ABC)

27,069

$458

$1,396

<$1,000 $1k–$5K $15k+

>$2

0k

<$2

k

$2k–$20k

3316

$33

$1,160

162

$17

$1,084

74,361

$31

$496

6390

$15

$471

92,518

$31

$188

24,323

$14

$148

77,919

$397

$693

57,851

$378

$245

7,003

$58

$1,191

69,974

$60

$566

44,293

$76

$201

6,648

$87

$1,215

35,155

$98

$608

22,385

$133

$203

$5,001–$10k $10,001–$15k

Revenue $ opportunity (50%)

~$25 million

(27,069 customers)

~$59 million

(119,722 customers)

~$26 million

(76,977 customers)

~$670 million

(4.7 million customers)

*Numbers reflect 10% sample size

*Bank ABC consumers with at least 1 open/active trade

#Customers / Avg Interchg / Avg Interest High Revolving balance

High

Sp

en

d

Knowing which accounts have the most revenue potential can be incorporated into existing strategies for maximum effect

Low


• Originations vs. existing accounts

• Score migration by: Geography, industry types, product types

• Historical risk performance

• Portfolio stability

Existing accounts

Metrics:

• VantageScore®

• Credit line

• Balance

• Spend and share of wallet

• Interest rate

• Credit usage (transactor, revolver, etc.)

Compare Bank ABC and peer group across several metrics to better understand:

• Strengths

• Area of opportunity

Use Case 3 – Portfolio benchmarking Benchmark Bank ABC against industry peers

659 661 663 663

727 728 729 730

300

400

500

600

700

800

201212 201312 201412 201512

Bank ABC Peer Group

Va

nta

ge

Sc

ore

® 3

.0

0%

10%

20%

30%

40%

2012 2013 2014 2015

Super Prime Prime Near Prime Subprime Deep Subprime

Bank ABC

0%

10%

20%

30%

40%

2012 2013 2014 2015

Super Prime Prime Near Prime Subprime Deep Subprime

Peer group


Q&A with Citi


Experian contact:

Natasha Madan [email protected]

Byron Evans [email protected]

Questions and answers


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How to overcome Big Data challenges...–Batch production ... 18 ©Experian 4/15/2017 Experian Public Vision 2017 Use Case 2 Targeting top of wallet off – Bank ABC spend Metrics

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