SlideShare a Scribd company logo
1 of 19
Download to read offline
Better Underwriting
Practices
Scott Brackin – Vice President – Auto Finance
(O):512-639-3282
2
So Which Path Did You Choose?
3
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
4
THE STATE OF CONSUMER CREDIT
M 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
300-499
6%
500-549
8%
550-599
10%
600-649
10%
650-699
13%
700-749
16%
750-799
18%
800-850
19%
“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
113M
of 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Big 3 bureaus, and further 19 million U.S. adults credit data is so limited or out of date with the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
5
• 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
6
• 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
7
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
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
9
• 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
10
ALTERNATIVE DATA ECOSYSTEM
11
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
12
• 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
UTILIZING CREDIT INFORMATION
V 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
13
• 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
14
Impac t of Alter native D ata on Tr aditional C r edit D ata
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
15
Questions? We have answers!
• 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
F A C T O R T R U S T
17
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
18
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
19
FactorTrust – Alternative Credit Bureau
• 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

More Related Content

What's hot

How do consumers feel about alternative credit data?
How do consumers feel about alternative credit data?How do consumers feel about alternative credit data?
How do consumers feel about alternative credit data?Experian
 
Microdecision Making in Financial Services - Greg Lamp @ PAPIs Connect
Microdecision Making in Financial Services - Greg Lamp @ PAPIs ConnectMicrodecision Making in Financial Services - Greg Lamp @ PAPIs Connect
Microdecision Making in Financial Services - Greg Lamp @ PAPIs ConnectPAPIs.io
 
Understanding Credit Reports and Credit Scoring (Webinar Slides)
Understanding Credit Reports and Credit Scoring (Webinar Slides)Understanding Credit Reports and Credit Scoring (Webinar Slides)
Understanding Credit Reports and Credit Scoring (Webinar Slides)NAFCU Services Corporation
 
Credit Scores: What's New?
Credit Scores: What's New?Credit Scores: What's New?
Credit Scores: What's New?milfamln
 
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
 
American Conference Institute: Delivering Financial Services to Underserved &...
American Conference Institute: Delivering Financial Services to Underserved &...American Conference Institute: Delivering Financial Services to Underserved &...
American Conference Institute: Delivering Financial Services to Underserved &...Mark F. Catone
 
Leveraging data, tech and analytics to improve collections
Leveraging data, tech and analytics to improve collectionsLeveraging data, tech and analytics to improve collections
Leveraging data, tech and analytics to improve collectionsExperian
 
A Holistic Approach to Property Valuations
A Holistic Approach to Property ValuationsA Holistic Approach to Property Valuations
A Holistic Approach to Property ValuationsCognizant
 
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banks
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-bankspredictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banks
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banksArup Das
 
Understanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsUnderstanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsSusan McCullah
 
Credit outlook for Millennials and Gen Z
Credit outlook for Millennials and Gen ZCredit outlook for Millennials and Gen Z
Credit outlook for Millennials and Gen ZExperian
 
Big Data in Banking (White paper)
Big Data in Banking (White paper)Big Data in Banking (White paper)
Big Data in Banking (White paper)InData Labs
 
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...CA Technologies
 
Credit Marketing in the Digital Age
Credit Marketing in the Digital AgeCredit Marketing in the Digital Age
Credit Marketing in the Digital AgeExperian
 

What's hot (16)

How do consumers feel about alternative credit data?
How do consumers feel about alternative credit data?How do consumers feel about alternative credit data?
How do consumers feel about alternative credit data?
 
Microdecision Making in Financial Services - Greg Lamp @ PAPIs Connect
Microdecision Making in Financial Services - Greg Lamp @ PAPIs ConnectMicrodecision Making in Financial Services - Greg Lamp @ PAPIs Connect
Microdecision Making in Financial Services - Greg Lamp @ PAPIs Connect
 
Lendit PostShow SlideShare
Lendit PostShow SlideShareLendit PostShow SlideShare
Lendit PostShow SlideShare
 
Understanding Credit Reports and Credit Scoring (Webinar Slides)
Understanding Credit Reports and Credit Scoring (Webinar Slides)Understanding Credit Reports and Credit Scoring (Webinar Slides)
Understanding Credit Reports and Credit Scoring (Webinar Slides)
 
Credit Scores: What's New?
Credit Scores: What's New?Credit Scores: What's New?
Credit Scores: What's New?
 
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...
 
Forecasting peer-to-peer lending risk
Forecasting peer-to-peer lending riskForecasting peer-to-peer lending risk
Forecasting peer-to-peer lending risk
 
American Conference Institute: Delivering Financial Services to Underserved &...
American Conference Institute: Delivering Financial Services to Underserved &...American Conference Institute: Delivering Financial Services to Underserved &...
American Conference Institute: Delivering Financial Services to Underserved &...
 
Leveraging data, tech and analytics to improve collections
Leveraging data, tech and analytics to improve collectionsLeveraging data, tech and analytics to improve collections
Leveraging data, tech and analytics to improve collections
 
A Holistic Approach to Property Valuations
A Holistic Approach to Property ValuationsA Holistic Approach to Property Valuations
A Holistic Approach to Property Valuations
 
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banks
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-bankspredictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banks
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banks
 
Understanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsUnderstanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage Professionals
 
Credit outlook for Millennials and Gen Z
Credit outlook for Millennials and Gen ZCredit outlook for Millennials and Gen Z
Credit outlook for Millennials and Gen Z
 
Big Data in Banking (White paper)
Big Data in Banking (White paper)Big Data in Banking (White paper)
Big Data in Banking (White paper)
 
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...
 
Credit Marketing in the Digital Age
Credit Marketing in the Digital AgeCredit Marketing in the Digital Age
Credit Marketing in the Digital Age
 

Viewers also liked

Viewers also liked (20)

matriz de memoria
 matriz de memoria matriz de memoria
matriz de memoria
 
Mock-up Event Program
Mock-up Event ProgramMock-up Event Program
Mock-up Event Program
 
Contratacion informatica y electronica
Contratacion informatica y electronicaContratacion informatica y electronica
Contratacion informatica y electronica
 
شهادة التخصصات
شهادة التخصصات شهادة التخصصات
شهادة التخصصات
 
1Titel BMW2011.pdf
1Titel BMW2011.pdf1Titel BMW2011.pdf
1Titel BMW2011.pdf
 
Unusual but potential agents of terrorists
Unusual but potential agents of terroristsUnusual but potential agents of terrorists
Unusual but potential agents of terrorists
 
Hair Loss Forum
Hair Loss ForumHair Loss Forum
Hair Loss Forum
 
Dining wall elevation B
Dining wall elevation BDining wall elevation B
Dining wall elevation B
 
Impacto Social 2008 Dianova
Impacto Social 2008 DianovaImpacto Social 2008 Dianova
Impacto Social 2008 Dianova
 
Fotografia ambiental – fauna – guará rubra
Fotografia ambiental – fauna – guará rubraFotografia ambiental – fauna – guará rubra
Fotografia ambiental – fauna – guará rubra
 
Estrategias de palavras-chave no ecommerce
Estrategias de palavras-chave no ecommerceEstrategias de palavras-chave no ecommerce
Estrategias de palavras-chave no ecommerce
 
StickyWebeCover2-1
StickyWebeCover2-1StickyWebeCover2-1
StickyWebeCover2-1
 
Test
TestTest
Test
 
Tice blog salto-dalzotto
Tice blog salto-dalzottoTice blog salto-dalzotto
Tice blog salto-dalzotto
 
Home Care Application
Home Care ApplicationHome Care Application
Home Care Application
 
compresores
compresorescompresores
compresores
 
CR_drug_abuse_2016
CR_drug_abuse_2016CR_drug_abuse_2016
CR_drug_abuse_2016
 
Pensamiento
PensamientoPensamiento
Pensamiento
 
Catheter Based Intervention and Surgical Management of Peripheral Arterial Oc...
Catheter Based Intervention and Surgical Management of Peripheral Arterial Oc...Catheter Based Intervention and Surgical Management of Peripheral Arterial Oc...
Catheter Based Intervention and Surgical Management of Peripheral Arterial Oc...
 
Pensamentos sábios e divertidos
Pensamentos sábios e divertidosPensamentos sábios e divertidos
Pensamentos sábios e divertidos
 

Similar to FactorTrust NABD Orlando2016

How To Scale Your Enterprise SEO Program
How To Scale Your Enterprise SEO ProgramHow To Scale Your Enterprise SEO Program
How To Scale Your Enterprise SEO ProgramSearch Engine Journal
 
DNBi Credit Enabling Sales | D&B
DNBi Credit Enabling Sales | D&BDNBi Credit Enabling Sales | D&B
DNBi Credit Enabling Sales | D&BDun & Bradstreet
 
Estimating Supply and Demand for Microcredit
Estimating Supply and Demand for MicrocreditEstimating Supply and Demand for Microcredit
Estimating Supply and Demand for MicrocreditFriedman Associates
 
Understanding Your Credit Report and Score
Understanding Your Credit Report and ScoreUnderstanding Your Credit Report and Score
Understanding Your Credit Report and ScoreSpringboard
 
Credit Scores-The Basics
Credit Scores-The BasicsCredit Scores-The Basics
Credit Scores-The BasicsBarbara O'Neill
 
Sbs Credit Services 2012 Presentation
Sbs Credit Services 2012 PresentationSbs Credit Services 2012 Presentation
Sbs Credit Services 2012 Presentationsbscredit
 
Consumer Lending: TRANSFORMED
Consumer Lending: TRANSFORMEDConsumer Lending: TRANSFORMED
Consumer Lending: TRANSFORMEDDave Buerger
 
Customer Analytics in Banking: Understand Your Customers
Customer Analytics in Banking: Understand Your CustomersCustomer Analytics in Banking: Understand Your Customers
Customer Analytics in Banking: Understand Your CustomersKavika Roy
 
6th November 2008 Final
6th November 2008 Final6th November 2008 Final
6th November 2008 FinalMarcusBrook
 
The Invaluable Fundamentals of Prepaid
The Invaluable Fundamentals of PrepaidThe Invaluable Fundamentals of Prepaid
The Invaluable Fundamentals of PrepaidVivastream
 
Digital Finance Use Cases
Digital Finance Use CasesDigital Finance Use Cases
Digital Finance Use CasesCGAP
 
Understanding your credit report
Understanding your credit reportUnderstanding your credit report
Understanding your credit reportrick zimmerman
 
How Big Data helps banks know their customers better
How Big Data helps banks know their customers betterHow Big Data helps banks know their customers better
How Big Data helps banks know their customers betterHEXANIKA
 
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's Economy
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's EconomyThe New Normal: How to Achieve Profitable C&I Loan Growth in Today's Economy
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's EconomyLibby Bierman
 

Similar to FactorTrust NABD Orlando2016 (20)

How To Scale Your Enterprise SEO Program
How To Scale Your Enterprise SEO ProgramHow To Scale Your Enterprise SEO Program
How To Scale Your Enterprise SEO Program
 
Archana Pradhan | Using Data for Advocacy
Archana Pradhan | Using Data for AdvocacyArchana Pradhan | Using Data for Advocacy
Archana Pradhan | Using Data for Advocacy
 
DNBi Credit Enabling Sales | D&B
DNBi Credit Enabling Sales | D&BDNBi Credit Enabling Sales | D&B
DNBi Credit Enabling Sales | D&B
 
Estimating Supply and Demand for Microcredit
Estimating Supply and Demand for MicrocreditEstimating Supply and Demand for Microcredit
Estimating Supply and Demand for Microcredit
 
Understanding Your Credit Report and Score
Understanding Your Credit Report and ScoreUnderstanding Your Credit Report and Score
Understanding Your Credit Report and Score
 
Credit Scores-The Basics
Credit Scores-The BasicsCredit Scores-The Basics
Credit Scores-The Basics
 
Sbs Credit Services 2012 Presentation
Sbs Credit Services 2012 PresentationSbs Credit Services 2012 Presentation
Sbs Credit Services 2012 Presentation
 
Consumer Lending: TRANSFORMED
Consumer Lending: TRANSFORMEDConsumer Lending: TRANSFORMED
Consumer Lending: TRANSFORMED
 
Fintech - MSME lending score card template for flow based lending
Fintech - MSME lending score card template for flow based lendingFintech - MSME lending score card template for flow based lending
Fintech - MSME lending score card template for flow based lending
 
Credit score
Credit scoreCredit score
Credit score
 
Customer Analytics in Banking: Understand Your Customers
Customer Analytics in Banking: Understand Your CustomersCustomer Analytics in Banking: Understand Your Customers
Customer Analytics in Banking: Understand Your Customers
 
6th November 2008 Final
6th November 2008 Final6th November 2008 Final
6th November 2008 Final
 
The Invaluable Fundamentals of Prepaid
The Invaluable Fundamentals of PrepaidThe Invaluable Fundamentals of Prepaid
The Invaluable Fundamentals of Prepaid
 
Digital Finance Use Cases
Digital Finance Use CasesDigital Finance Use Cases
Digital Finance Use Cases
 
Understanding your credit report
Understanding your credit reportUnderstanding your credit report
Understanding your credit report
 
How Big Data helps banks know their customers better
How Big Data helps banks know their customers betterHow Big Data helps banks know their customers better
How Big Data helps banks know their customers better
 
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's Economy
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's EconomyThe New Normal: How to Achieve Profitable C&I Loan Growth in Today's Economy
The New Normal: How to Achieve Profitable C&I Loan Growth in Today's Economy
 
CIBIL PPT.pptx
CIBIL PPT.pptxCIBIL PPT.pptx
CIBIL PPT.pptx
 
Revised Credit
Revised CreditRevised Credit
Revised Credit
 
Revised Credit
Revised CreditRevised Credit
Revised Credit
 

FactorTrust NABD Orlando2016

  • 1. Better Underwriting Practices Scott Brackin – Vice President – Auto Finance (O):512-639-3282
  • 2. 2 So Which Path Did You Choose?
  • 3. 3 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
  • 4. 4 THE STATE OF CONSUMER CREDIT M 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 3 300-499 6% 500-549 8% 550-599 10% 600-649 10% 650-699 13% 700-749 16% 750-799 18% 800-850 19% “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 113M of 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
  • 5. 5 • 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
  • 6. 6 • 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
  • 7. 7 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
  • 8. 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
  • 9. 9 • 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
  • 11. 11 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
  • 12. 12 • 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 UTILIZING CREDIT INFORMATION V 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
  • 13. 13 • 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
  • 14. 14 Impac t of Alter native D ata on Tr aditional C r edit D ata 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
  • 15. 15 Questions? We have answers! • 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
  • 16. F A C T O R T R U S T
  • 17. 17 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
  • 18. 18 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
  • 19. 19 FactorTrust – Alternative Credit Bureau • 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