1. The document summarizes the analysis of loan data including data exploration, model building, and actionable insights. 2. Logistic regression was found to perform similarly to random forest models but with less complexity. 3. Further analysis of default rates by loan grade showed opportunity to better discriminate risk between borrowers depending on grade. 4. Next steps discussed automating the lending strategy using a "LendingBot" and questions around the ethics of using zip code data in underwriting.

1. Roland Nelet
South Norwalk, November 11th
2015
CaseStudy
for

2. Summary
1. Dataexploration/datatransformation
2. modelbuilding
3. actionableinsights
4. nextsteps/Q&A

3. DATaexploration
datatransformation

4. ● Smooth relationship with
default rates
● Small inventory for
riskier loans
gradesarestrong
predictorsoFdefault

5. ● Problematic for two reasons :
○ Low information value for
outliers
○ small sample on outliers
● Binning + Categorical
highlyskeweddistributions
PublicRecords
DELINquencylast 2years
Inquirylast6months

6. NAY YAY"id" "member_id"
"funded_amnt" "funded_amnt_inv"
"grade" "sub_grade"
"emp_title" "issue_d"
"pymnt_plan" "url"
"desc" "title"
"zip_code" “addr_state”
"open_acc" "revol_bal"
"initial_list_status" "out_prncp"
"out_prncp_inv" "total_pymnt"
"total_pymnt_inv" "total_rec_prncp"
"total_rec_int" ”policy_code"
"total_rec_late_fee" "recoveries"
"collection_recovery_fee" "last_pymnt_d"
"last_pymnt_amnt" "next_pymnt_d"
"last_credit_pull_d"
"collections_12_mths_ex_med"
"mths_since_last_major_derog"
"mths_since_last_delinq"
"mths_since_last_record"
"loan_amnt" "term"
"int_rate" "installment"
"emp_length" "total_acc"
"home_ownership" "annual_inc"
"verification_status" "loan_status"
"purpose" "dti"
"delinq_2yrs" "inq_last_6mths"
"revol_util" "pub_rec"
"earliest_cr_line" "revol_bal"

7. modelbuilding

8. ● Random Forest performs
marginally better than
Logistic Regression
● The extra complexity is
not justified by the small
performance improvement.
● Bottomline : Logistic
Regression offers a good
compromise between
accuracy and complexity
LogisticRegression
vs.randomforest
ROCCurve

9. Distributionsof
default
Densityplotbygrade
● Wide probability distributions
● Better opportunity to
discriminate between borrowers
for riskier grades
● F and G grades have the same
risk profile

10. actionableinsights

11. ● Default Rate is not the
relevant metric for an
investor
● Avoiding risk is not a
profitable trading
strategy
● Each trading strategy is
a trade-off between
expected return,
scalability and risk
tolerance
Astatisticaltradingmodel
Expected
Return
default
rate
Liquidity

12. backtest:gradesa,b,c,d
IRR Inventory

13. backtest:gradesE,F,G
IRR
Inventory

14. nextsteps
Q&A

15. nextsteps:lendingbot
● Automate the execution : LendingBot
● Include recent data, target delinquent loans as well as
“charged-off” .
● Use a scheduler to place orders automatically 4 times a
day.
● Place orders in a segregated portfolio and scale after a
monitoring period of 3 months.
Q&A/talkingpoint
● Is it ethical for a lender to use Zip Code data in its
underwriting policy ?