Lending Club Case Study

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 ?