Insikt - Data Driven NYC (27)

Insikt.com ©2013 Insikt Inc. All Rights Reserved. Private and Confidential.

Insikt: Alternative Credit!

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Data Driven Conference!

May 20, 2014!


ALTERNATIVE CREDIT: WHY NOW?!

BACKGROUND!

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NEW REGULATIONS HAVE CHANGED LENDING IN THE U.S. FOR GOOD!

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BANKS

Dodd Frank1

2010 + 5 = 2015

Basel III 2018 + 5 = 2023

Basel II/II.5 2012 + 5 = 2017

Card Act 2009 + 5 = 2014

Revises Regulatory Capital Definition Capital requirements

+ Liquidity requirements + Stress testing

= Lower Risk Appetite and Balance Sheet Reduction

Addresses Accountability, Responsibility & Disclosure

Interest rate hike limitation + Longer notification period + Late fee limitation

= Lending Decrease

Addresses Wall Street Reform & Consumer Protection

Asset level data disclosure + Capital requirements + Risk retention requirements + Proprietary trading prohibition + Ring fencing + Removes credit rating reliance

= Lower Risk Appetite and Balance Sheet Reduction Addresses Credit, Operational,

& Market Risk Risk weighting categories

+ Implements VAR + Capital charges + Trading book limitations

= Lower Risk Appetite and Balance Sheet Reduction

“Elevated supervisory stringency impacts total loans/loan capacity for 20 quarters after the onset of the tighter supervisory standards”

—SBA Office of Advocacy

1 Conflicts with Basel III requirements


REVOLVING CREDIT AVAILABILITY HAS DECLINED SIGNIFICANTLY SINCE 2008!

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Revolving Credit (Home Equity & Credit Card) Limits & Balance1 !

$2.0T

$1.0T $2.5T

$3.0T

2013 2012 2011 2010 2009 2008 2007 2006 2005 2004

2003

$0.5T 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004

2003

Banks have reduced both the available & outstanding amounts of revolving consumer credit!

$930B

$3.5T $1.5T

$360B

$4.0T $2.0T Available! Outstanding!

1 Source: FRBNY Consumer Credit Panel/Equifax


SMALL BUSINESS LENDING BY BANKS HAS ALSO FALLEN!

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Lending by Depository Lenders to Small Businesses 2003-20121!

750 700

600 550

650

2012!2011!2010!2009!2008!2007!2006!2005!2004!2003!

$120B

1 Source: United States Department of Commerce, Bureau of Economic Analysis

Small firm bank lending rose from $308bn (1994) to $659bn (2008) but declined from 2008 to 2012 by $120B!


AMONG CONSUMERS, NON PRIME BORROWERS HIT THE HARDEST

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$(50)

$(40)

$(30)

$(20)

$(10)

$-

$10

$20

$30

2007 2008 2009 2010 2011 2012

Change to Revolving Credit Extended to Borrowers with FICO <660 ($B)!

$120B reduction in nonprime credit card

availability (2009-2012)!

Data Source: Jefferies Equity Research


TECHNOLOGY IS CHANGING LENDING!

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Application Underwriting Servicing Payments Better Lending

More Profitable

•  Higher Volume •  Higher Conversion •  Lower CPA

•  Lower Servicing Costs •  Lower Losses

Online Applications

•  Faster Process •  Better Execution •  Reduced Friction

Increased Volume Lower CPA

Big Data •  More Data Sources •  Better Segmentation •  Faster Trend Analysis

Lower Risk Increased Uptake

ACH Payment System •  Lower Cost •  Faster Tracking/

Reconciliation •  Faster Trend Analysis

Lower Cost, Lower Risk

Mobile •  SMS Payment •  Reminders •  Mobile Barcodes •  3rd Party Locations

Lower Cost, Lower Risk

Driving Improvements across the Value Chain!


NON-BANK, ALTERNATIVE LENDERS EMERING TO FILL THE VOID!

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All Loan Products Are Being Transformed!

Business!Secured Business Installment

Unsecured Business Installment Merchant Cash Advance

Receivable Factoring!Secured Real Estate

Purchase Order Financing Leases!

Consumer!Unsecured Installment Unsecured Revolving

Rent to Own!Payday

Online Title Lending!Student Loans

Secured Real Estate Leases!


MY START: ALT CREDIT BACK IN 2005!

BACKGROUND!

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STARTED IN A SUPERMARKET:1st Progreso Finaniero Store!

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CHALLENGE: HOW DO WE HELP MARIA AND MAKE HER A LOAN?!

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Hard working, wants the America dream, but doesn’t have a traditional data in a credit bureau or a FICO score…!

!…And No FICO = No Approval and worse, No Identity!

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NO FILE THIN FILE THICK FILE (540 AVG. FICO)

Challenging Population to Underwrite!

§  E-HARMONY APPROACH: Start with Robust Application §  Application data is very valuable

§  BOOK SOME BAD LOANS: Invest in loans before data scientists §  Most valuable data is performance data §  1000 “bads” helps to build a model

§  ALTERNATIVE DATA: Merge alternative data with bureau data

§  Not only for scoring, conversion is also important §  Adverse action reasons / regulation B

LESSONS LEARNED!

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500,000 LOANS LATER…HAPPY CUSTOMERS AND SINGLE DIGIT LOSSES!


INSIKT APPROACH IN P2P LENDING!

DATA FOR LENDERS!

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Data and Analytics are core to both sides of our business!

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Origination Technology!(B2B, white label)!

Investors!(B2C, branded)!

Underwriting / Selection of Loans:!!(1)  Risk Model Development!(2)  Loan Valuation

Framework!(3)  Stress Resilience!

Enhanced transparency and access to data for investors:!!(1)  Bond Performance!(2)  Loan Performance!


Prosper Case Study!

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Significantly Better in Bad Times! Consistent and Steady in Good Times!25!

20!

15!

10!

5!

Months since origination!0!

1! 9! 18! 27! 36! 1! 9! 18! 27! 36!

2008!Financial Crisis!

2009-11!Averaged!

Cum

ulat

ive

loss

rate

%!

!Prosper!

!Insikt!

!

Months since origination!

100 years of Experience. Robust Models.!Goal = Outperform Prosper in Good & Bad times!


(1) Risk Model Development!

Developing our Underwriting / Loan Selection Strategy!

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•  Max data attributes available!

•  Scorecard-ed logistic regression used to balance sloping power and processing speed!

•  Risk Model used to create discrete risk segments with differentiated performance expectations!

!•  NPV & ROE

optimization!

•  Apply observed loss rate performance through 2008 recession NPV valuations as economic stress test!

(2) Loan Valuation Framework!

(3) Valuation Stress Testing!


(1) De-average key performance dimensions as much as possible!

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§  Data is the most important part of your model build §  Test early and often §  Many things can influence your outcomes, incorporate as many as

possible into your testing strategy: §  Price §  Credit Line §  Marketing Channels

§  Incorporate data from a variety of sources: §  Clean your data! Many insights are buried in missing values and error

codes


(1) Risk Decision Tree Combining Multiple Risk Model Dimensions

§  Insikt leverages a decision tree to effectively combine multiple risk models across unique population segments and data sources

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(1) Optimize KS!

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Insikt V2 PMI V4 PMI V5 FICO

2011(PMIv5 Eligible) 29.60 22.06 24.02 9.88

20121H(PMIv5 Eligible) 27.11 18.97 25.30 13.79

20122H(PMIv5 Eligible) 27.73 23.09 21.67 9.08

KS comparison


(2) Valuation Framework -- Loss rates!

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0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

AA 2011 selected 2011 non-selected

2012 selected 2012 non-selected

0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

A 2011 selected 2011 non-selected


0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

B 2011 selected 2011 non-selected


0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

D* 2011 selected 2011 non-selected


0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

E* 2011 selected 2011 non-selected


0.00%

5.00%

10.00%

15.00%

20.00%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

C 2011 selected 2011 non-selected



(2) Apply valuation framework at the most granular level (optimize marginal decisions)!

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§  Value your decisions across your population dimensions and your product dimensions

§  Granular ROE Assessment across credit grades, model deciles, and credit line buckets:

Base Case IRR2010 -‐ 2012 Loss Performance & Current Pricing Strategy

0-‐$5,000 Loan Amounts $5,001-‐$10,000 Loan Amounts $10,001-‐$14,999 Loan Amounts1 2 3 4 5 6 7 8+ 1 2 3 4 5 6 7 8+ 1 2 3 4 5 6 7 8+

AA 7% 3% -‐10% 14% -‐21% 8% 15% 16% AA 7% 4% -‐3% -‐8% 16% 14% 16% -‐108% AA -‐3% 0% -‐1% -‐39% 15%A 32% 19% 17% 4% -‐20% -‐18% -‐37% -‐21% A 23% 16% 0% 5% 6% -‐11% -‐50% -‐42% A 10% 20% 18% 2% 18% 35% 42%B 57% 31% 31% 17% 28% -‐6% -‐20% -‐31% B 15% 38% 47% 30% 22% -‐21% -‐31% -‐13% B 61% 56% 48% 26% 64% 13% -‐91% 59%C 74% 61% 50% 45% 44% 27% 12% 0% C 31% 31% 41% 64% 1% 43% -‐24% -‐39% C -‐135% 79% 53% 15% 28% 85% -‐1%D 120% 98% 65% 46% 30% 67% 10% D 110% 67% 61% 49% 27% 39% -‐59% D 114% 99% 113% 116% -‐101% -‐31% 99%E 150% 70% 57% 92% 80% 77% 51% E 155% 149% 106% 81% 53% 47% 25% E 155% 147% 149%HR 112% 166% 89% 25% 48% HR -‐13% 105% -‐2% 118% HR 169%

Stress Case IRR2x Base Loss Performance & Current Pricing Strategy


AA 6% -‐4% -‐30% 14% -‐55% 1% 15% 16% AA 5% -‐2% -‐18% -‐28% 16% 14% 16% -‐230% AA -‐13% -‐10% -‐12% -‐88% 15%A 30% 4% -‐2% -‐29% -‐78% -‐76% -‐113% -‐84% A 12% -‐1% -‐36% -‐26% -‐25% -‐60% -‐139% -‐120% A -‐14% 8% 0% -‐30% 0% 35% 42%B 57% 4% 3% -‐25% -‐4% -‐72% -‐101% -‐122% B -‐29% 18% 36% 2% -‐16% -‐101% -‐121% -‐88% B 61% 56% 39% -‐6% 64% -‐36% -‐241% 59%C 74% 43% 20% 10% 8% -‐29% -‐58% -‐83% C -‐15% -‐14% 3% 49% -‐78% 5% -‐131% -‐162% C -‐350% 79% 28% -‐50% -‐27% 85% -‐86%D 120% 80% 19% -‐23% -‐53% 21% -‐93% D 110% 19% 6% -‐16% -‐61% -‐36% -‐231% D 114% 99% 113% 116% -‐315% -‐174% 99%E 150% -‐11% -‐34% 35% 12% 6% -‐47% E 155% 149% 63% 13% -‐46% -‐52% -‐99% E 155% 147% 149%HR 61% 166% 12% -‐114% -‐70% HR -‐196% 44% -‐170% 70% HR 169%

Historical Vintage Sample Size


AA 196 176 72 20 18 7 3 3 AA 192 187 87 41 8 7 2 4 AA 55 60 31 12 1A 95 295 217 164 118 62 40 31 A 112 246 222 164 86 59 32 30 A 33 67 57 32 22 6 5B 28 139 177 185 165 123 77 62 B 17 100 127 120 115 98 53 43 B 2 17 24 21 11 11 3 3C 7 91 188 242 241 250 193 225 C 6 51 99 113 121 142 102 113 C 2 7 12 16 6 2 3D 8 32 67 76 123 103 165 D 2 24 18 36 47 56 72 D 1 2 2 4 4 3 1E 3 14 24 31 79 64 127 E 1 4 14 22 24 23 49 E 1 2 4HR 4 3 9 19 46 HR 5 7 6 18 HR 1

Base Case IRR2010 -‐ 2012 Loss Performance & Current Pricing Strategy


AA 7% 3% -‐10% 14% -‐21% 8% 15% 16% AA 7% 4% -‐3% -‐8% 16% 14% 16% -‐108% AA -‐3% 0% -‐1% -‐39% 15%A 32% 19% 17% 4% -‐20% -‐18% -‐37% -‐21% A 23% 16% 0% 5% 6% -‐11% -‐50% -‐42% A 10% 20% 18% 2% 18% 35% 42%B 57% 31% 31% 17% 28% -‐6% -‐20% -‐31% B 15% 38% 47% 30% 22% -‐21% -‐31% -‐13% B 61% 56% 48% 26% 64% 13% -‐91% 59%C 74% 61% 50% 45% 44% 27% 12% 0% C 31% 31% 41% 64% 1% 43% -‐24% -‐39% C -‐135% 79% 53% 15% 28% 85% -‐1%D 120% 98% 65% 46% 30% 67% 10% D 110% 67% 61% 49% 27% 39% -‐59% D 114% 99% 113% 116% -‐101% -‐31% 99%E 150% 70% 57% 92% 80% 77% 51% E 155% 149% 106% 81% 53% 47% 25% E 155% 147% 149%HR 112% 166% 89% 25% 48% HR -‐13% 105% -‐2% 118% HR 169%

Stress Case IRR2x Base Loss Performance & Current Pricing Strategy


AA 6% -‐4% -‐30% 14% -‐55% 1% 15% 16% AA 5% -‐2% -‐18% -‐28% 16% 14% 16% -‐230% AA -‐13% -‐10% -‐12% -‐88% 15%A 30% 4% -‐2% -‐29% -‐78% -‐76% -‐113% -‐84% A 12% -‐1% -‐36% -‐26% -‐25% -‐60% -‐139% -‐120% A -‐14% 8% 0% -‐30% 0% 35% 42%B 57% 4% 3% -‐25% -‐4% -‐72% -‐101% -‐122% B -‐29% 18% 36% 2% -‐16% -‐101% -‐121% -‐88% B 61% 56% 39% -‐6% 64% -‐36% -‐241% 59%C 74% 43% 20% 10% 8% -‐29% -‐58% -‐83% C -‐15% -‐14% 3% 49% -‐78% 5% -‐131% -‐162% C -‐350% 79% 28% -‐50% -‐27% 85% -‐86%D 120% 80% 19% -‐23% -‐53% 21% -‐93% D 110% 19% 6% -‐16% -‐61% -‐36% -‐231% D 114% 99% 113% 116% -‐315% -‐174% 99%E 150% -‐11% -‐34% 35% 12% 6% -‐47% E 155% 149% 63% 13% -‐46% -‐52% -‐99% E 155% 147% 149%HR 61% 166% 12% -‐114% -‐70% HR -‐196% 44% -‐170% 70% HR 169%

Historical Vintage Sample Size


AA 196 176 72 20 18 7 3 3 AA 192 187 87 41 8 7 2 4 AA 55 60 31 12 1A 95 295 217 164 118 62 40 31 A 112 246 222 164 86 59 32 30 A 33 67 57 32 22 6 5B 28 139 177 185 165 123 77 62 B 17 100 127 120 115 98 53 43 B 2 17 24 21 11 11 3 3C 7 91 188 242 241 250 193 225 C 6 51 99 113 121 142 102 113 C 2 7 12 16 6 2 3D 8 32 67 76 123 103 165 D 2 24 18 36 47 56 72 D 1 2 2 4 4 3 1E 3 14 24 31 79 64 127 E 1 4 14 22 24 23 49 E 1 2 4HR 4 3 9 19 46 HR 5 7 6 18 HR 1


(3) Stress test your assumptions to understand sensitivities!

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§  What is the biggest risk to your strategy? §  Valuation sensitivity §  Historical Volatility

§  Develop scenarios based on historical volatility §  e.g. 2008 Economic Downturn


INSIKT DATA & TOOLS FOR INVESTORS!

DATA FOR INVESTING!

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DATA FOR INVESTORS: Info that actually matters and tools a CRO would use

§  Access to detailed performance data §  Static pool history, loan level reporting, and ability to compare across

platforms §  Access to detailed bond performance data

§  Full cash flow breakdown §  Performance flags to identify variance concerns

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MYCRO TOOL

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