Intuit's Diane Chang talks about using Rule Ensembles to predict credit risk at the 2015 Grace Hopper Celebration of Women in Computing Conference.

1. 2015
Using Rule Ensembles
to Predict Credit Risk
Diane Chang, Intuit
October 16, 2015
#GHC15
2015

2. 2015
Trade-off: speed vs. rates

3. 2015
Can accounting data help?

4. 2015
Low credit risk?

5. 2015
Traditionally, one big model
Annual Revenue
≤ $100K > $100K
In TX Not in TX RetailNon-retail
State Industry
… …

6. 2015
Ensemble methods

7. 2015
Lots of little trees

8. 2015
Trees  Rules
Time in Business
≤ 3 yrs > 3 yrs
> 8 > 10%≤ 10%
# Invoices Sales Growth
≤ 8

9. 2015
Trees  Rules
Time in Business
≤ 3 yrs > 3 yrs
# Invoices Sales Growth
Rule 1: Time in Business ≤ 3 yrs
Rule 2: Time in Business > 3 yrs
Rule 3: Time in Business ≤ 3 yrs and # Invoices ≤ 8
Rule 4: Time in Business ≤ 3 yrs and # Invoices > 8
1 2
Rule 5: Time in Business > 3 yrs and Sales Growth ≤ 10%
Rule 6: Time in Business > 3 yrs and Sales Growth > 10%
≤ 8
3
> 8
4
≤ 10%
5
> 10%
6

10. 2015
A lot of rules!
Rule 1
Rule 2
Rule 3
Rule 4
Rule 5
Rule 6
…
Rule 1
Rule 2
Rule 3
Rule 4
…
Rule n

11. 2015
Which rule is most predictive?
w1* R1 + w2* R2 + w3* R3 + … + wn* Rn
Weights, wi’s,
computed via
logistic regression

12. 2015
Scoring
https://en.wikipedia.org/wiki/Logistic_regression
loge
p
1- p
p =
1
1+e-score
score = loge
p
1- p
= w0 + w1R1 + w2R2 +...+wnRn

13. 2015
Example
R1: Time in Business ≤ 3 yrs
R2: Time in Business > 3 yrs
R3: Time in Business ≤ 3 yrs & Invoices ≤ 8
R4: Time in Business ≤ 3 yrs & Invoices > 8
R5: Time in Business > 3 yrs & Sales Growth ≤ 10%
R6: Time in Business > 3 yrs & Sales Growth > 10%
✓
✗
✗
✗
✗
✓
score = -2 + 0.1 − 0.6 = -2.5
Make a loan offer!
• 2 years in business
• 10 invoices per month
• Sales growth: 12%
score =-2+0.1R1 -0.25R2 +0.14R3 -0.6R4 +0.01R5 -0.07R6
p =
1
1+e2.5
» 0.08

14. 2015
Rule ensembles RULE
Rule
Ensemble
Logistic
Regression
✔ ✖
✔
✔
✖
✖
Missing data
High predictive power
Large number of variables

15. 2015
Try it yourself!
 Professor Friedman’s website:
− http://statweb.stanford.edu/~jhf/R_RuleFit.html
 Open source “wrapper” to RuleFit
− https://github.com/intuit/rego

16. 2015
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