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TRANSCRIPT
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
3-Y
ear
Stu
de
nt
Loan
De
fau
lt R
ate
(%
)
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
De
fau
lt R
ate
(%
)
0
3
6
9
12
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
of
bo
rro
we
rs
Borrower age
Upstart
Lending Club