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© 2012 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc.

Other product and company names mentioned herein are the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified,

or distributed in any form or manner without the prior written permission of Experian.

Experian Public.

Creative data products and services for intelligent business expansion

Jacqueline Maybaum | Experian

© 2012 Experian Information Solutions, Inc. All rights reserved.

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Agenda

Background and business challenge

Approach

► Expert design

► Business intelligence and validation study

► Loss projections and sensitivity analysis

Additional complexities

► Program readiness

The winning combination

Applying results


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Background and

business challenge


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Background

Large regional bank with close to $30 billion in assets and $16 billion in deposits would like to enter the credit card market

Start with a small NBA tied affinity card

Plans to ramp up with general purpose card offering across full geographic footprint

Plans to pursue “road to credit” strategy to service all ends of the credit spectrum

No infrastructure and very limited human capital with specific revolving terms and credit card product industry talent


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Business challenge

Validation target market assumptions

Determine scale

Risk management experience focused on closed-end lending products

Required heavy revolving credit-specific industry expertise

Timing needed to align with sports season and team momentum


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Collaborative

consulting approach


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Applying best practices


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Workshops

Identify and roadmap all requirements for launch

Ensure program readiness (gap focus)

Determine risk structure and desired loss tolerance

Profile target market

Pre-launch benchmarking and validation

Meet accelerated timeline expectations

Best practice originations policy for day one

Identify tracking mechanism and develop success metrics

Define and agree on objectives Business review process


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Analytic approach


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Competitive intelligence

profiling and validation study


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Competitive intelligence and validation study

Observation

period

Jan-Mar 2009

random sample

21–23 month performance window

12/10 12/09 12/08


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501–700 29%

701–760 19%

761–780 5%

781–800 5%

801–820 5%

821–860 10%

861–990 27%

Exclusion

VantageScore® Distribution in footprint

801 – 990

42%

Credit Profiles Consumers with bankcards in footprint

Reviewed credit characteristics and score distributions within the asset footprint

Evaluated surrounding expansion markets

Studied overall credit profiles as well as those with new bankcard trades


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1.07

0.88

0.78

0.81

0.78

0.75

0.71

0 0.5 1 1.5

501–700

701–760

761–780

781–800

801–820

821–860

861–990

Number of new bankcard inquiries

Credit Profiles Consumers with new bankcards in footprint

Length of credit history

Modeled Income

Debt-to-income ratio

Bankcard trades and balances

Major derogatory items

Delinquency 63.26%

40.33%

32.59%

31.78%

27.43%

22.73%

13.49%

0% 20% 40% 60% 80%

501–700

701–760

761–780

781–800

801–820

821–860

861–990

Average utilization new bankcards

Average credit limit new bankcards

635

729

770

791

811

841

925

0 500 1000

501–700

701–760

761–780

781–800

801–820

821–860

861–990

Average VantageScore® new bankcards

$1,298.9

$2,759.0

$3,714.0

$4,201.3

$4,455.1

$4,991.3

$6,226.6

$0 $2,000 $4,000 $6,000 $8,000

501–700

701–760

761–780

781–800

801–820

821–860

861–990

Average credit line new bankcards


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Usage profiles Within score bands

Validate target market assumptions and affinity to “high spend” payment products

457

20,105

19,620

238

21,592

48,962

74,027

14,370

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000

Consolidator

Inactive

Mixed

No hit

Rate surver

Revolver

Transactor

Unclassified

Total number of consumers


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Delinquency and loss performance Applying assumptions

Global policy rules applied Y

Policy rule 1 > 24

Policy rule 2 > 2

Policy rule 3 < 3

Policy rule 4 > 20

Policy rule 5 <= 0.40

Policy rule 7 Y

Policy rule 11 Y

Policy rule 12 Y

Policy rule 13 Y


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Row labels

Sum of

#Good

Sum of

#Bad

Total

accounts

Unit

Bad%

Cum

unit Bad

Cum

Dollar

Bad

860–990 14,075 62 14,137 0.44% 0.44% 1.82%

840–859 2,367 35 2,390 1.46% 0.59% 2.03%

820–839 2,254 42 2,282 1.84% 0.74% 2.39%

800–819 2,232 54 2,270 2.38% 0.92% 2.73%

780–799 2,150 72 2,202 3.27% 1.14% 2.78%

760–779 2,166 65 171 2.92% 5.51% 2.84%

740–759 2,148 44 151 1.99% 1.96% 2.77%

720–739 2,127 117 134 5.22% 1.96% 2.89%

700–719 281 14 85 4.75% 1.96% 2.84%

501–699 2,117 416 140 16.43% 1.96% 3.55%

Grand total 31,917 84 5,801 1.45%



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VantageScore® cutoff 780

Number starting 23,369

Number booked 8,880

Approval rate 38%

Average balance 1,387

Average credit line assignment 7,521

Total credit line exposure 66,788,083

Total balances 12,319,000

Cumulative unit bad % 1.14%

Cumulative dollar bad % 2.78%

Tier 02

Applicant distribution A

Gross response rate B


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Additional complexities


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Launch readiness


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Developed code to identify thousands of consumer files that met over 140 combinations of credit policy rules, credit line assignment and pricing tier criteria and subsequently ran through over 700 test cases to identify defects

Testing Accelerated framework


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How did it work?


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The winning combination


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Business consulting

Years of experience launching and managing co-branded credit card programs

Analytics

The ability to identify and understand geographic and risk dynamics of the target market

Fifty-day implementation of a conservative market entry plan designed to allow for controlled expansion

The recommended approach blended analytical expertise for data driven strategy design with business consulting expertise, providing the bank the

ability to intelligently enter the credit card arena

The winning combination!


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Applying results


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Competitive business intelligence and profiling lead to actionable results

Example of output Business intelligence output for auto lender


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On-us

auto trade

Off-us

auto trade

Peer group

auto trade

Average income ($) 110,337 100,664 121,761

Average credit score 727 713 704

Age of oldest trade (years) 19.1 19.9 19.3

Average number of open trades per consumer 8.9 6.8 11.2

Average bankcard revolving debt 8,680 6,510 9,967

Average bankcard credit line utilization 14% 12% 17%

Average number of open auto trades per consumer 1.3 1.0 1.2

Average balance per auto trade 14,108 13,926 13,499

Average estimated APR for auto trades 6.96 8.31 7.92

Percent of open auto trades 30+ DPD 1.14% 2.49% 2.25%

Percent of open auto trades 60+ DPD 0.54% 1.61% 1.24%


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Market share expansion Business intelligence output for financial institution



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Workshops

As key decision makers, you need to be able to test into new markets using sound, proven practices

Do not miss out or delay expansion opportunities!

There are proven techniques to help ensure that your expansion approach meets your objectives without taking unneeded risks

Creative data products and services exist and will help you to take advantage of business expansion and new market opportunities at the optimal time

Intelligent business expansion Summary


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For additional information, please contact:

[email protected]

Vision Expert Annex Open every morning, afternoon and during session breaks

Meet session speakers

Gather information on Experian products and services

Request research and complimentary materials

Schedule one-on-one meetings with Experian experts

May 6–9 • The Phoenician • Scottsdale, Ariz.


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creative data products and services for intelligent … data products and services for intelligent...

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