welcome to a post-fico world!...y 2014jun 2014jul 2014ug 2014sep 2014oct 2014v 2014dec 2014an...
TRANSCRIPT
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Welcome to a Post-FICO World!
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Consumer credit modeling relies on data and analytics that haven’t changed in decades
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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
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So why doesn’t everyone do it?
Real data science is hard
Regulatory risk is daunting
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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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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
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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
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Data that is predictive in a recession is even more valuable
Unemployment rate by level of education
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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
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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%
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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
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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%
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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%
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“Sounds great, but my lawyers say no!”
- You
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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:
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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:
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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:
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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
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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