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Better Underwriting Practices Scott Brackin – Vice President – Auto Finance (O):512-639-3282

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Page 1: FactorTrust NABD Orlando2016

Better Underwriting Practices

Scott Brackin – Vice President – Auto Finance(O):512-639-3282

Page 2: FactorTrust NABD Orlando2016

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So Wh ich Pa th D id You Choose?

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AGENDA

– State of the Operator’s Consumer & Credit Data

– Key Components of Good Underwriting

– The value and types of alternative credit data

– Utilizing Credit Information

– Questions we can answer

Page 4: FactorTrust NABD Orlando2016

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THE STATE OF CONSUMER CREDITM A R K E T O P P O R T U N I T Y T O S TAY C O M P E T I T I V E

Underserved by Big 3

300-4996%

500-5498%

550-59910%

600-64910%

650-69913%

700-74916%

750-79918%

800-85019%

“Big 3” ~$8.5B in 2014 Revenue – Serving the “Prime/Banked”

Credit and Consumer Data Services Market

For Underbanked / Non-prime Largely Untapped

Massive Underserved Market Population

113Mof US adults, have a FICO score

under 700

FICO Score Distribution of US Population (240M)

With ~$8.5B in combined revenue, the Big 3 credit reporting agencies (“CRAs”) are

focused on covering prime US consumers, leaving a significant market opportunity in the

underbanked population that is non-prime.

From CFPB (*May 2015) – 26 million U.S. adults have no credit history with the

Big 3 bureaus, and further 19 million U.S. adults credit data is so limited or out of date with the

Big 3 bureaus, that they are unscoreable. In total, 45 million U.S. adults are living without credit

scores.

47% OF U.S. ADULTS HAVE BELOW-AVERAGE CREDIT SCORES

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• Three Sides to Every Deal

– Capacity

• Highly debated metric

• Can the buyer afford the unit?

• 46% of Americans can not cover a $400 emergency – Federal

Reserve

– Collateral

• Insuring the valuation is fair to both buyer and lender - CFPB

– Credit Data

• Traditional

• Alternative

K E Y C O M P O N E N T S O F G O O D U N D E R W R I T I N G

GOOD UNDERWRITING = KEEPING THEM SOLD

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• Credit Data - Not Every Operator Uses Credit Information

– “Everyone of my customers has bad credit”

– “Regulatory requirements are steep & expensive”

– “I don’t want to lose my customer to the automotive finance companies”

K E Y C O M P O N E N T S O F G O O D U N D E R W R I T I N G

GOOD UNDERWRITING = KEEPING THEM SOLD

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SHOULD YOU USE CREDIT INFORMATION ?

Know the endgame and the more likely

your objective will be met.

Considerations:

• qualify more consumers

• reduce credit losses

• improve conversion rates

• risk adjust pricing

GOOD UNDERWRITING = KEEPING THEM SOLD

Page 8: FactorTrust NABD Orlando2016

V a l u e & T y p e s o f

A L T E R N A T I V E D A T A

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• Everything that is non-Big 3 bureau based has been

considered alternative…not true

• Runs the entire data spectrum

• Proprietary Data to widely available data

• Non-prime trade lines to public record data

• FCRA (decision-able) to non-FCRA (not decision-able)

• Real time reported data to monthly reported data

• Alternative Bureau to Big 3 Bureau

• What’s really important? Predictive lift and business benefit

ALTERNATIVE DATA SPECTRUM

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ALTERNATIVE DATA ECOSYSTEM

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VALUE OF ALTERNATIVE CREDIT DATA N O N - T R A D I T I O N A L V S T R A D I T I O N A L

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• Credit data can provide you with identity intelligence and verification insights

that allow you to make sure the borrower’s:

– Input data is current and accurate

– SSN was issued and belongs to the name of the borrower

– Name verifications, e.g. marriage licenses

– Physical addresses and date verification

– Birth date verification

UTIL IZ ING CREDIT INFORMATIONV E R I F Y O R VA L I D AT E A N A P P L I C A N T ’ S I D E N T I T Y

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• 25% of the

underbanked applied

with a different mobile

number within one year

• 34% changed

employers within one

year

• 36% moved within one

year

• The more of each…the

higher the risk

UNDERBANKED INDEX I N S I G H T S I N T O T H E S TA B I L I T Y O F U N D E R B A N K E D C O N S U M E R S

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I m p a c t o f A l t e r n a t i v e D a t a o n Tr a d i t i o n a l C r e d i t D a t a

Traditional bureau overlaid

FactorTrust data on their

bureau database:

• For ‘Unscoreable’ files,

adding short-term loan

data would increase the

bureau score into the 600

range, and

• Adding a delinquent short-

term loan would increase

the bureau score into the

500 range

36.5%

45.7%

19.0%

14.9%

29.8%

25.0%

37.2%

63.4%

4.0%

0.5%

0.5%

0.7%

9.7%

5.7%

0.0%

50.3%

80.4%

84.6%

69.5%

65.3%

57.0%

0% 20% 40% 60% 80% 100%

< 500

500 - 600

600 - 700

700 - 800

800 - 900

> 900

Total

2009 - 2011 Generic Bureau Score Shift Distribution

Score Decrease No Change Total Score Increase

Short-term Borrowers

2009 - 2011 Credit Score Shift Distribution

Page 15: FactorTrust NABD Orlando2016

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Quest ions? We have answ ers !

• Questions:

– Overview of the dilemma operators face using traditional credit bureau

data and trying to underwrite people with known traditional bureau dings

(low score/no score)

– What does alternative data mean? How does it impact an operator? How

you can improve credit defaults?

– Application data vs credit vs collateral.

– How to protect customers from losing them to dealers using larger sub-

prime finance companies

COME SEE FACTORTRUST @ BOOTH #110

Page 16: FactorTrust NABD Orlando2016

F A C T O R T R U S T

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COMPANY OVERVIEW

FactorTrust, The Alternative Credit Bureau, helps lenders

give underbanked consumers the credit they deserve by

providing the most unique source of alternative credit

information not available from the Big 3 bureaus

Operates in the US and UK, with a growing proprietary

global database of 200 million transaction records on 17

million unique consumers, representing one of the largest

databases in the underbanked industry

Reciprocity-driven business model

Real-time data reporting

Founded: 2006

Headquarters: Atlanta, GA

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PRODUCT MIX & STRATEGY C R E D I T L I F E C Y C L E S O L U T I O N S

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Fac to rTrus t – A l te rna t i ve C red i t Bu reau

• Dedicated & Proven auto finance experience

• No CFPB Violations

• Regulatory compliance

• Loan performance – additive, loan debt not in the big “3”

• Pay for performance pricing

• Rapidly expanding – 400K Monthly

• Analytics – Lend Protect Auto model

– Custom portfolio model

– Waterfall Data Process

• Credit data expertise– 150+ years