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Author Topic: Lending Club loan default prediction model question

l
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Lending Club loan default prediction model question
OP: February 10, 2019, 12:00:00 AM
I've built a loan default prediction model with Lending Club about 2 years ago and I've been investing modestly with it since then.  I'm getting about 5.5 to 6% adj. return on my loans.  So I think its working fairly well.  I'm trying to improve the model hopefully one day achieve 10% returns.  I'm wondering if anyone else has built similar models and have come up with creative variable transformations on the historical loan data?  Here are some that I've come up with

loan_to_income = loan amount / income
payment_to_income = installment / income
time_since_earliest_credit_line = earliest credit line date - issue date
open_acc_ratio = open_acc / total_acc
curr_bal_ratio = tot_cur_bal / total_bal_ex_mort

some of these are more or less predictive.  Anyone have any other interesting transforms?

My inspiration for developing a Lending Club model came from LendingRobot  http://blog.lendingrobot.com/research/predicting-the-number-of-payments-in-peer-lending/
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R
  • Posts: 484
Lending Club loan default prediction model question
#1: December 31, 1969, 07:00:00 PM
I recommend the book "Credit Scoring, Response Modeling and Insurance Rating" by Steven Finlay.
Also recommend the statistical package R as it's free, open source and very powerful.
Finally, I recommend the following LA thread and particularly the post by brycemason 12/23/2015.
In particularly note the referral to "the four horsemen of the consumer credit scoring apocalypse".
https://forum.lendacademy.com/index.php/topic,3570.msg31594.html#msg31593

Anyway, test transformations for covariance with your other model factors to see if they statistically add value;
loan_to_income (one of your transformations) is (was) one of the four biggies.
Good luck with that 10%!

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Lending Club loan default prediction model question
#2: December 31, 1969, 07:00:00 PM
Three important transformations are:
  • Installment to Income
  • Loan Amount to Revolving Balance
  • Credit Age (Earliest Credit Date - Loan Issue Date)
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T
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Lending Club loan default prediction model question
#4: December 31, 1969, 07:00:00 PM
Installment to income represents capability to pay, what does loan amount to income represent?

from: Rob L on February 11, 2019, 10:30:33 AM
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R
  • Posts: 17
Lending Club loan default prediction model question
#6: December 31, 1969, 07:00:00 PM
Our Data Scientist, Guangming Lang, used machine learning to mine the LC historical data. He used a combination of R and XGBoost to train our Liquid P2P loan selection models. I believe these are one click installs on AWS if you're inclined to tackle such a project.

https://liquidp2p.com/
https://www.linkedin.com/in/gmlang/
https://www.r-project.org/about.html
https://xgboost.readthedocs.io/en/latest/
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T
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Lending Club loan default prediction model question
#7: December 31, 1969, 07:00:00 PM
Thanks for all of the replies.  I should have shared a little about myself and my methods.  I have experience building predictive credit models in financial institutions.  My primary tool of choice to build predictive models is R.  I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies". 
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T
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Lending Club loan default prediction model question
#8: December 31, 1969, 07:00:00 PM
Maybe you and Guangming should have a chat... lol. I’m a serial entrepreneur, not a data scientist. I knew what I wanted to build and assembled a team. He obviously was a critical team member. Guangming also authored a book on scoring consumer credit. I would be happy to show and discuss some of his work in detail if you want to pm me.


Sent from my iPhone using Tapatalk
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T
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Lending Club loan default prediction model question
#11: December 31, 1969, 07:00:00 PM
Did you use installment/income  term in addition to loan amount/income and FICO in your multivariate logistic regression? Did you also use separate monthly income term in your regression? If not, then your statement is ingenuous as you didn't considered the relative importance of these terms in respect to each other. If you had considered relative merits of these terms together in your regression, you would know that monthly income is a very important "borrower characteristics" datapoint and any transformation containing monthly income will be weighted heavily in a regression. The first step of any regression analysis is to identify important and influential attributes to include in the regression.

The English language explanation for loan amount/income transformation is simple. This transformation represents whether a borrower given certain income can pay back the loan amount or not irrespective of duration. The installment/income transformation represents whether a borrower given certain income can make regular payment of installment amount over certain duration to payback loan amount or not. It is a "borrower indebtedness" datapoint and goes along with DTI.

When you are lending on LC primary market, you are deciding whether to lend on the LC given terms of lending (interest rate, duration installment). If you were deciding the terms of lending yourself (for ex: Prosper 1.0), then your strategy of not considering platform recommended terms of lending in assessing the loan quality will be effective and you will come up with your own acceptable terms of lending at which you will lend.

Sorry to see you discontinue the lending but not surprised.

from: Rob L on February 12, 2019, 10:15:42 AM
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T
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Lending Club loan default prediction model question
#14: December 31, 1969, 07:00:00 PM
So, you had no theoretical basis/reason for excluding "installment/income" in favor of "loan amount/income" from your model. That's all I wanted to highlight as a forum participant reached out to me offline for more clarification on merit of using installment over loan amount. I typically don't get into back and forth on internet forums. Thanks for your time in explaining the reasoning.
 
from: Rob L on February 15, 2019, 11:36:31 AM
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