Welcome to a Post-FICO World!

Consumer credit modeling relies on data and analytics that haven’t changed in decades

A smarter prime lender could approve almost twice as many borrowers and yet have fewer defaults

Traditional Underwriting Modern Data Science

0%

20%

40%

60%

80%

100%

Average lender approval rates*

Defaults

Percent in US with loans but have

never defaulted**

* Source: Prosper, Lending Club **Source: Upstart data study with TransUnion

So why doesn’t everyone do it?

Real data science is hard

Regulatory risk is daunting

So you want to add a new variable?

• Broadly available

• Decade+ of training data

• Easily verifiable

• Unbiased and legal

Hint: Facebook is not the answer!

Some helpful attributes

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Loan

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(%

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School ranking

15

10

5

800 1000 1200 1400 1600

We’ve assembled a collection of variables that are more predictive than the entire credit bureau file

20

Default rate of “best 40%” from sample population

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fau

lt R

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(%

)

0

3

6

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15

Random Financial variables Financial variables Obtained a degree

Financial variables Obtained a degree

School ranking Major

Financial variables Obtained a degree

School ranking Major

SAT/GPA

Data from NCES National Education Longitudinal Study

And by layering all of these variables together, we can make smarter credit decisions instantly

Data that is predictive in a recession is even more valuable

Unemployment rate by level of education

A disruptive credit model requires unique predictive data, better math, and faster learning

Traditional Upstart

Variables Credit file • Income Credit file • Income • Occupation • Employer • Work Experience • Degrees • Schools • GPA • Test Scores •

Job Offers • Cost of Living • etc.

MethodsBlack/white decision logic,

simple regression

Continuous decision logic, cross-validated logistic regression, higher-order variables, random forest,

monte carlo methods, ensemble learning

Learning Speed

Lenders 2-3x per year,

FICO 2-3x per decade

Automated training,

daily updates

When you’re building a disruptive credit model, verification of inputs is essential

Upstart

Borrower income verified 100%

Borrower education verified 100%

Borrower savings verified 100%

Verification phone call 100%

0%

5%

10%

15%

20%

25%

30%

MAY 2

014

JUN

2014

JUL

2014

AUG 2014

SEP 2014

OCT 2

014

NOV 2

014

DEC 2014

JAN

2015

FEB 2015

MAR 2

015

APR 2015

MAY 2

015

JUN

2015

JUL

2015

AUG 2015

SEP 2015

OCT 2

015

NOV 2

015

DEC 2015

JAN

2016

Approval Rate of Control Group IRR by Origination Month

Proof in the pudding: steadily increasing approval rates and consistent investor returns

Our model has learned quickly, with each cohort performing better than the prior

Cohort # Originated % DQ121+

Q3 2014 852 5.40%

Q4 2014 1559 4.49%

Q1 2015 2365 2.88%

Q2 2015 3356 2.68%

Q3 2015 5109 1.23%

Q4 2015 7163 0.06%

Our delinquencies by loan grade also provide evidence that we’re accurately pricing our loans

Loan Grade # Originated Average Age (Months) % DQ121+ Modeled %

DQ121+

AAA 21 12.6 0.00% 0.02%

AA 1391 10.7 0.14% 0.15%

A 5052 9.8 0.61% 0.46%

B 4639 10.4 2.00% 1.31%

C 2578 9.7 2.48% 2.22%

D 3795 9.1 3.98% 3.70%

E 639 5.4 0.94% 0.94%

“Sounds great, but my lawyers say no!”

- You

So you give loans to wealthy grads from elite

schools?

No. Less than 2% of Upstart borrowers come from elite schools. And wealthy people don’t need our loans.

Q:

A:

Your average borrower is 28 years old - are you

biased against older borrowers?

No. In fact, all else being equal, an applicant with longer credit history will get a lower rate on Upstart.

Q:

A:

Does your system discriminate against people based on race, gender, or other protected classes?

No. Using a tool provided by the CFPB, we were able to demonstrate that our model demonstrates no statistical

bias with respect to race or gender.

Q:

A:

XFinancial Capacity to Repay

Propensity to Repay( (=

All successful credit models are based on the same tried & true concepts

fIncome

• Earning potential • Unemployment potential

Expenses

• Debt obligations • Living expenses • Spending habits

Assets

• Available to service debt

Personal Characteristics

• Credit history • Personal responsibility • Awareness of credit score

Support Network

• Network connectedness • Backstop financial support

… but modern data science can make these concepts better

Success in our case means reducing the price of credit to 65M underserved borrowers

Pe

rce

nt

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rro

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Borrower age

Upstart

Lending Club

Thank you!

!

"

dave@upstart.com

@davegirouard