Vision 2014: How Can Experian's Trended Capabilities Help Solve Lender Issues?

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How can Experian’s trended capabilities help solve lender issues?

Bryan Collins Experian

Brodie Oldham Experian

#vision2014

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

Key lender challenges

Creditworthiness

Risk

Profitability

Achieve YOUR goals with trended capabilities


Top lender questions

Who is likely to respond to an offer?

How does a consumer use credit?

Is there a better way to understand risk?

How can I acquire profitable consumers?

Achieve YOUR goals with trended capabilities


Trended SolutionsSM

Experian’s trended universe Overview

24 months of

trended data

Custom attributes Historical balance and payment data utilized to create attributes / build models

Trended

attributes

Trend ViewSM Identify revolving / transacting trades

Short-Term AttributesSM Capture bal / util changes

Line Management AttributesSM Identity limit changes

Payment Stress AttributesSM Aggregate payment data

Consumer

applications

Experian TAPSSM Annual credit card spend

Balance Transfer IndexSM Propensity to balance transfer

In the Market ModelsSM Propensity to open auto, mortgage, bankcard

EIRC for RevolvingSM Effective APR and 6-month yield

Risk Stability IndexSM Improving or declining risk score


A new view of the consumer Experian uses trended data to deliver a unique view of the consumer

Payment behavior:

MIN PAY – 2% Annual card

spend:

$7,500

6 month yield:

$ 74

Average effective APR

16.99%

In the market?

Bankcard

BT Index (0-999)

875 BTs in last 12 months

2

Revolving trades:

4

Transacting trades:

0

Risk Index rating:

Unstable

Utilization in last 6 months:

+ 35%

Credit card bals last 6 months:

+ $8,000

BEHAVIOR

RISK REVENUE

RESPONSE

Trended data allows Experian to reveal a comprehensive view of a consumer’s credit behavior to improve risk, response and profit…


FCRA compliant

Full life cycle application

► Available in batch (prescreen, account review) and online

10 + years experience

Experian innovative generic and custom solutions

Trended solutions Why Experian?


Who is likely to

respond to an offer?

Trended capabilities

Client issue 1


Enhanced segmentation, unique Experian assets and sophisticated modeling methodology were used to develop a series of best-in-class prospecting models

► Developed with Premier AttributesSM, the credit industry’s most advanced and comprehensive set of predictive consumer credit attributes

► Leveraged Experian’s Trend ViewSM data for increased prediction

► Patent pending for unique approach to scorecard development

In the Market ModelsSM Unique modeling for optimal predictive metrics

Suite of next generation prospecting scores that predict the likelihood that a consumer will open a specific product type 30 to 120 days from the point of observation


In the Market ModelSM Bankcard model performance

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

Pe

rce

nt

of

op

en

ac

co

un

ts

Percent of Population

The new In the

Market ModelSM

for Bankcard

identifies 8%

more consumers

who opened a

new trade over

the legacy model

at 50% of the

scored population

80% of opens

identified in the

Top-50%

ITMM 1.0

ITMM 2.0

ITMM = In the Market ModelSM


Key components

Balance Transfer IndexSM Overview

Model

target

Experian tradeline

level data and

variables

Historical consumer balance

transfer activity

Model predicts likelihood that a consumer will initiate a balance transfer within the subsequent six months

Score out put from 100-990 Higher score = higher likelihood of balance transfer

Generic trended and custom attributes utilized

Key predictors: Balance transfer history, historical balance transfer amounts, cards carried utilized

Experian developed logic identifies historical card to card balance transfer activity using Experian tradeline level data


Balance Transfer IndexSM

Value proposition

Other banks Bank XYZ Balance transfer in to client accounts

Enhance targeting of existing balance transfer customers and eliminate marketing on customers with low balance transfer likelihood

Opportunity to adjust terms of offer based on customer balance transfer appetite

Other banks Bank XYZ Balance transfer out of client accounts

Identify customer with high risk of balance attrition

Pre-empt balance attrition with retention offers and capture balance transfer dollars being funneled to other banks

Other banks Balance transfer between non-client accounts by client customers (60% of balance transfer $ activity is “off-bank”)

Identify new balance transfer opportunities within customer base

Grow capture rate of total customer balance transfer activity

Other banks

Balance Transfer IndexSM creates value for clients in various ways


How does a consumer

use credit?


Client issue 2


Trended data Experian retains 24 months of history on key data fields on every trade

Trade #1

Bank name Account # Open date Credit line Balance

Actual

payment Loan type

Visa 4812 3/9/1990 $25,800 $13,300 $650 Credit card

Account

condition

Account

status

Special

comment

Months

reporting

Minimum

payment due

Months since

update

Date of

payment

OPEN CURR ACCT CH 231 $275 1 Aug-13

Trade #1 Balance Credit limit

Minimum

payment due

Account

payment

Date of

payment

Month 0 $13,300 $25,800 $275 $1,000 Aug

Month 1 $14,680 $25,800 $280 $1,000 July

Month 2 $16,060 $25,800 $286 $1,000 June

Month 3 $17,440 $25,800 $297 $1,000 May

Month 4 $18,820 $25,800 $310 $1,000 April

Month 5 $20,200 $25,800 $330 $1,000 March

Month 6 $21,580 $25,800 $343 $1,000 February

Month 7 $22,960 $25,800 $350 $1,000 January

These five

historical fields

plus the

corresponding

trade level data

are used

to create

Experian’s

Trended

SolutionsSM


Trend ViewSM Understanding behavior to satisfy consumer credit needs

… results should drive offer strategy to improve response and optimize marketing expense

Precise targeting

Identify consumers who have been revolving for many months for balance transfer offers

Target revolvers with consolidation / personal loans

Target transactors and “super transactors” with reward offers

Target inactives based upon multiple behavioral uses (market with product rotation)

Create campaign efficiency suppression (suppress “card collectors” and “rate surfers”

Examples of attributes

Number of 12-month revolving trades

Average balance of 12-month revolving trades

Recent balance transfer

Number of balance transfer in last 24 months

Number of 12-month transacting trades

Number of dropped trades


Balance Transfer Surfer Model™ Overview

Marketing eligible population

Balance Transfer IndexSM

Balance Transfer

Surfer Model™

Balance transfer

consumer target

Balance Transfer IndexSM predicts likelihood that a consumer will initiate a balance transfer within the next six months. Score output from 100-990

Higher score = higher likelihood of BT

Balance Transfer Surfer Model™ identifies consumers that are likely rate surfers. Score output from 100-989

Higher score = higher likelihood of being a rate surfer

Identify consumers who are likely to perform a balance transfer in the next six months

for marketing and retention efforts


$-

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Apr May Jun July Aug Sept Oct Nov Dec

Bankcard balance Retail balance

Short-Term AttributesSM Capture balance and utilization changes

Attribute examples Quantity

Number of revolving trades with greater than 0% utilization change in three months 2

Dollar change in revolving balances in six months $5,776

Percentage change in total revolving balances in nine months 210%

Balance and utilization changes

Total limit

By looking at short-term trends you can identify rapidly increasing balances

Capturing the dollar and percentage changes will enhance your risk models and identify risky customers before they go delinquent


Is there a better way to

understand risk?


Client issue 3


Experian stores historical payments, balances and requested payment data at the trade level

The data enables a detailed view of changes in pay rates and debt burdens that are not captured in credit scores, providing a unique profile of a consumer

Payment Stress AttributesSM Viewing payment data, both currently and over time, to understand consumer risk

February 2012

VantageScore® 720

Balance $22,000

Status Good standing

Balances Min Pay DUE Actual pay

Jan $22,000 $550 $2,000

Dec $26,000 $650 $3,500

Nov $20,000 $500 $12,000

Oct $28,000 $700 $8,000

Sep $35,000 $875 $6,000

Aug $45,000 $1,125 $11,000

Jul $48,000 $1,200 $3,000


Jan $22,000 $550 $550

Dec $19,000 $475 $475

Nov $16,000 $400 $400

Oct $15,000 $375 $400

Sep $11,500 $288 $320

Aug $9,000 $225 $300

Jul $6,500 $163 $200

Consumer A Consumer B





February 2012

VantageScore® 720

Balance $22,000



Jan $22,000 $550 $2,000

Dec $26,000 $650 $3,500

Nov $20,000 $500 $12,000

Oct $28,000 $700 $8,000

Sep $35,000 $875 $6,000

Aug $45,000 $1,125 $11,000

Jul $48,000 $1,200 $3,000

Consumer A

Paying well over minimum payments

Demonstrated ability to pay

No stress





February 2012

VantageScore® 720

Balance $22,000



Jan $22,000 $550 $550

Dec $19,000 $475 $475

Nov $16,000 $400 $400

Oct $15,000 $375 $400

Sep $11,500 $288 $320

Aug $9,000 $225 $300

Jul $6,500 $163 $200

Consumer B

Making minimum payments for the last three months

Payment amounts vs. minimum due is decreasing over time

Payment stress

$550 $550

$475 $475

$400 $400

$375 $400

$288 $320

$225 $300

$163 $200


Differentiating consumers through payments

Who is the best prospective customer?

Payment data enables a much better understanding of how a consumer uses their bankcard accounts

Consumer Avg

monthly

balance

Vantage

Score®

Monthly balance and payments Total

annual

payments

Avg % of

balance

paid

A $3,462 740 $1,126 2.6%

B $3,435 736 $22,390 53.4%

C $3,465

744 $42,429 100.0%

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11

Total Bankcard Balance

Total Bankcard Payments

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000




$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000





What are they?

Payment Stress AttributesSM compare a consumer’s actual payment against the requested minimum payment due (card, auto, mortgages) in the most recent month, as well as over time

How are they implemented?

The attributes can be returned in both batch and online (10/1), are FCRA compliant and can be used for adverse action

Lenders can leverage this data to:

Enhance risk segmentation strategy across the consumer credit life cycle

Understand ability to pay

Target consumers based on payment behaviors

Analysis: Payment Stress AttributesSM Overview

Key attribute examples Value

$$ of total payments in last month $250

Percent of payments vs. balances 4%


Historical scores calculated using trade line trended data that is displayable and disputable

Eighteen historical monthly scores scaled to a traditional risk model

Four Indices showing stability over time

Risk Stability IndexSM

Using trended data that is displayable on a current consumer credit report, Experian has created a Risk Stability Index

800

700

600

500 Jan Feb Mar Apr Dec May Jun Jul Aug Sep Oct Nov

Mary Stable

Bob Possible stress

Sue Improving

Three people. One score. Three different ways they got there.


Historical risk score changes can differentiate current risk

Use for targeting, segmenting, and decisioning

Risk stability index

Current

Vantage

Score® Overall Very stable Stable

Moderately

stable Unstable

Severely

unstable

Bad rate Bad rate Bad rate Bad rate Bad rate Bad rate

600 – 700 9.67% 6.20% 7.88% 10.07% 11.00% 12.58%

Index identifies less risky consumers based on stability of credit score over time

Standard deviation over 18 months


How can I acquire

profitable consumers?


Client issue 4


Experian TAPSSM is a spend algorithm built from credit data

Experian TAPSSM uses longitudinal credit data to proxy the last 12 months of spend on credit and charge cards

It calculates spend, separately, on each trade line, then aggregates those trade lines to provide a snapshot of an individual's annual spend

Experian TAPSSM Product overview

Experian TAPSSM captures >90%

of all credit card spend

VISA

MasterCard

American Express

Discover

Trade lines

“General purpose type credit cards”

*Source: Nilson December 2011

Purchase Volume* 2012 (Bil) Share

Cre

dit

card

typ

es

Visa $809.3 39%

Master Card $479.3 23%

Amex $476.3 23%

Discover $107.2 5%

Store $127.9 6%

Oil $47.0 2%

Other $5.2 <1%

Total $2,052.2 100.0%

$1,000 $0 $8,000

$100 $12,000

$0

$3,000

Annual

spend

$24,100

Annual spend on each card

$0

=


Experian TAPSSM accurately captures spend

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

1 2 3 4 5 6 7 8 9 10

Avera

ge s

pen

d i

n $

Predicted spend decile

Experian TAPSSM algorithm performance (validation sample)

Predicted Spend

Actual Spend

Balance

The Experian TAPSSM algorithm discriminates spend, accurately and independent of balances, with R2 of 0.75-0.85


Experian has developed more than 300 trended attributes, but clients often want to aggregate the “raw” data in other ways

Experian can export an archive of trade level trended data for clients to:

► Derive / create specs of new attributes for Experian to code and deliver in batch or on line environments

► Utilize the raw trended data as an input source for a model

More than half of the largest U.S. banks have developed and implemented their own trended attributes to better understand consumer behavior

Trended SolutionsSM Custom attributes


Similar to regular credit attributes that describe:

A quality

Or feature

Or characteristic inherent in a credit report

Revenue proxies are elements that can be calculated to extend a credit report

They can be inferred or derived but are created specifically to proxy elements that drive income:

► Annual spend

► Revolving balance

► Yield

► Effective APR

► From which interchange and interest income can be calculated – revenue

What are revenue proxies?


Profit suite – revenue proxies Experian TAPSSM and EIRC for RevolvingSM

Experian TAPSSM is a special algorithm

that uses longitudinal credit data to

proxy the last 12 months of spend on

credit and charge cards

Key fields used:

► Total annual plastic spend

► Wallet share

► Payment rate

Over 60 validations

► R2 exceeds 0.80

Experian TAPSSM

Total annual plastic spend

EIRC for RevolvingSM is a yield

algorithm that uses longitudinal credit

data to proxy the last six months of

interest on credit cards

Key fields used:

► Average effective APR (%)

► Average interest ($)

► Average daily balance

► Average revolving balance

Over 60 validations

► R2 exceeds 0.90

EIRC for RevolvingSM

Estimated interest rate calculator

for revolving trends


Until now card issuers have had limited, actionable insight into how their customers or prospects have used their competitor’s cards

How much do they spend? How much interest do they pay, at what rate?

This detail is now available, providing segmentation that quickly reveals prospects who have the potential to deliver meaningful profitability

U.S. credit card revenue segments Interchange and interest / fee revenue

2012 <$75

Low

$75 – $500

Medium

>$500

High

<$5,000

Low

830 807 784

$883 $2,255 $2,519

$4 $238 $1,421

$5,000 –

$20,000

Medium

875 857 810

$11,159 $10,946 $10,823

$10 $234 $1,962

>$20,000

High

894 885 848

$51,158 $49,979 $61,180

$9 $246 $2,216

Interest paid on all cards

Card

sp

en

d

Segment

average

VantageScore®

Experian TAPSSM

Interest paid


Over 30% of U.S. cardholders produce a little interest or interchange revenue

Surprisingly, they receive almost as many prescreen offers as other prospects

Revenue segment distribution

2012 <$75

Low

$75 – $500

Medium

>$500

High

<$5,000

Low 32% 7% 6%

$5,000 –

$20,000

Medium

13% 8% 9%

>$20,000

High 12% 6% 7%

Interest paid on all cards C

ard

sp

en

d

32% of the U.S.

population are

generating limited

revolving interest

revenue or

interchange

revenue from

spend

U.S. population distribution VantageScore® >700


Are you interested in a validation?

What’s next? Road mapping…

Coming soon

EIRCSM versions

Trended data in credit

reporting

TAPSSM and EIRC

RevolvingSM attributes


…with the power of trended solutions!

Understanding consumers’ past behavior is crucial to understanding credit preferences and potential risks and for developing a strategy for the future

Enhance the profitability of your portfolio…


For additional information, please contact:

[email protected]

[email protected]

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Vision 2014: How Can Experian's Trended Capabilities Help Solve Lender Issues?

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