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1

Investors - LC / Recovery Payment Received for Charged Off Loans

« Last post by AnilG on Today at 12:15:46 AM »

Hello,

Do any readers have notes from following loans in their portfolio? I am looking for information on how much recovery amount were they paid on these charged off loans. Original amount lent, amount charged off, and recovery amount paid information on any notes from following list of Loan ID is appreciated.

Thanks.

Code: [Select]

[1]   187383   365044   446879   545408   628186   667933   722727   786973   801181   820963   834642
[12]   888558   985214  1125962  1334517  1390923  1469058  1548720  1587199  1606429  2234851  2286330
[23]  3158374  3216977  3635121  3919282  4255787  4282031  6707722  7059592  7648944  8614949  9845644
[34] 10597591 12987029 19256502 24055069 30836182 32069035 32280008 33201307 35004113 35733650 36099806
[45] 37741722 39531903 39977574 40776754 41071468 41388698 44056470 46764136 48181956 48556047 51978290
[56] 55987171 57015193 60025467 60246818 63919895 64037882 64967871 65611490 65821312 66491455 67446038
[67] 70702343 70841513 71744763 75304312 75427297 76403791 80662890 84051666 88084249 88402158 90244701
[78] 90835542 91575843

2

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by larrydag on February 18, 2019, 05:53:00 PM »

Good luck to you rawraw.  You'll find auto finance to be a rewarding and challenging industry

3

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by Rob L on February 17, 2019, 09:12:27 AM »

There are a lot of opportunities to get started in auto finance if you have the right skillsets.  The typical skillsets that auto finance companies look for are STEM degrees and business degrees.  You can easily look up on a job aggregator to see the job descriptions.  Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans.  If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience.  Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position.  The important things to know in auto finance is credit bureau data and loan portfolio management.

Getting started in predictive modeling is more broad.  There is a huge swath of companies and industries looking for that skill.  In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization.  Here is the secret untold story about predictive modeling that most academics do not tell you.  Predictive modeling is 80% data acquisition and management and 20% modeling.  So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data.  That means having skills in SQL, Python, R or other data programming tool.   Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis.  Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.   

Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting.  Do it in baby steps if you have no applied math background.  Start with basic statistics 101 and move on to more advanced.

Thanks. I have broad experience in the lending and data analysis. But I've started to learn python and may be moving into a role that is for an auto lender.  Should be fun if it happens

Does sound like fun. Good luck!

4

Investors - LC / Re: Tue 02/12/19 payments about 3 times higher than normal

« Last post by Fred93 on February 17, 2019, 12:30:45 AM »

Thursday used to be the big day.  (payments due on weekend days rolled in).  Perhaps with the new payment timing that has moved to Tuesday.

5

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by rawraw on February 16, 2019, 08:50:58 PM »

There are a lot of opportunities to get started in auto finance if you have the right skillsets.  The typical skillsets that auto finance companies look for are STEM degrees and business degrees.  You can easily look up on a job aggregator to see the job descriptions.  Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans.  If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience.  Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position.  The important things to know in auto finance is credit bureau data and loan portfolio management.

Getting started in predictive modeling is more broad.  There is a huge swath of companies and industries looking for that skill.  In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization.  Here is the secret untold story about predictive modeling that most academics do not tell you.  Predictive modeling is 80% data acquisition and management and 20% modeling.  So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data.  That means having skills in SQL, Python, R or other data programming tool.   Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis.  Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.   

Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting.  Do it in baby steps if you have no applied math background.  Start with basic statistics 101 and move on to more advanced.

Thanks. I have broad experience in the lending and data analysis. But I've started to learn python and may be moving into a role that is for an auto lender.  Should be fun if it happens

6

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by Rob L on February 16, 2019, 09:22:46 AM »

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".

Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.

1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?

TIA

My modeling method is using a Cox Prop Hazard multivariate survival model tuned with GLMNET.  Nothing really special.  I've never put a survival model in production and wanted to give it a go.  I've worked in auto finance for the last 7 years and have done applied math and data analysis for most of my career.  I've built credit scoring models for large lenders.  It is actually quite fun in my opinion.

1) to be honest I do it as a hobby.  I've only invested a few thousand in the last couple of years.  I'm doing it to keep my chops up and it interests me.
2) yes I'm using the API.  I don't invest enough frequency to see if speed is important
3) 36: B 10%, C 33%, D 17%, E 14%     60: B 2%, C 11%, D 4%, E 5%, F/G 2%

Thanks for the reply. As I mentioned earlier it was a very nice hobby for me as well. If you had been lucky enough to stumble across LC back in 2013 you could have made some serious money with your skills. Plenty of good loans from which to select. From the previous post in this thread by mikedev10 there's just not very many loans offered any more.

7

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by larrydag on February 16, 2019, 08:33:29 AM »

There are a lot of opportunities to get started in auto finance if you have the right skillsets.  The typical skillsets that auto finance companies look for are STEM degrees and business degrees.  You can easily look up on a job aggregator to see the job descriptions.  Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans.  If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience.  Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position.  The important things to know in auto finance is credit bureau data and loan portfolio management.

Getting started in predictive modeling is more broad.  There is a huge swath of companies and industries looking for that skill.  In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization.  Here is the secret untold story about predictive modeling that most academics do not tell you.  Predictive modeling is 80% data acquisition and management and 20% modeling.  So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data.  That means having skills in SQL, Python, R or other data programming tool.   Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis.  Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.   

Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting.  Do it in baby steps if you have no applied math background.  Start with basic statistics 101 and move on to more advanced. 

8

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by rawraw on February 16, 2019, 07:45:38 AM »

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".

Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.

1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?

TIA

My modeling method is using a Cox Prop Hazard multivariate survival model tuned with GLMNET.  Nothing really special.  I've never put a survival model in production and wanted to give it a go.  I've worked in auto finance for the last 7 years and have done applied math and data analysis for most of my career.  I've built credit scoring models for large lenders.  It is actually quite fun in my opinion.

1) to be honest I do it as a hobby.  I've only invested a few thousand in the last couple of years.  I'm doing it to keep my chops up and it interests me.
2) yes I'm using the API.  I don't invest enough frequency to see if speed is important
3) 36: B 10%, C 33%, D 17%, E 14%     60: B 2%, C 11%, D 4%, E 5%, F/G 2%

Are there any resources you'd recommend for someone starting in auto finance and predictive modeling?

9

Investors - LC / Re: Tue 02/12/19 payments about 3 times higher than normal

« Last post by anabio on February 16, 2019, 06:10:41 AM »

If I remember correctly LC stated that they were going to start crediting loan payments on the date they were due. If that was on the 12th then on the 12th you probably got credited for payments that would have been delayed about 4 days...so on the 12th you probably got payments that were scheduled for Feb 12, Feb 11, Feb 10, Feb 9.

10

Investors - LC / Re: Lending Club loan default prediction model question

« Last post by AnilG on February 16, 2019, 01:15:37 AM »

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.
 

Installment / Income wasn't used for the reason I mentioned before, loan amount / income was. (Actually Installment / Income was used in early models but somewhere along the way Bryce noted the problem regarding the use of Installment and replaced it with loan amount / income.) IIRC the change didn't have a major effect on the model results.
Yes, income and loan amount were also included separately.

The objective of the model was to produce results very much Prosper 1.0, yielding an independent probability of default (i.e "risk"). In addition to the risk model a measure of "reward" was also computed (using the LC assigned interest rate, etc.). Prosper 1.0 offered no comparative "reward" basis which IMO is why it failed. Using risk and reward it was simple enough to rank a set of loans LC offered from best to worst and purchase only the ones ranked best. Of course this determination is all relative. If all the loans are lousy then selecting the best ones will still be lousy; and vice versa. Back in 2013 and 2014 there were lots of very good loans (as we now know from hindsight). Picking the relatively best ones was a winning bet. As time moved forward risk / reward increased and I had to chose whether to accept less reward for risk or buy fewer loans. Unfortunately I lowered my lending standards and accepted less reward for the risk. I had no idea it would get as bad as it did. Had I not lowered my lending standards my guess is that I would have completely stopped purchasing LC D&E notes in 2015. My bad. Actually the 16Q2 fiasco saved me from myself as it caused me to stop purchasing loans, sell half of my loan portfolio and reassess. I did buy a few more higher risk (D&E) loans in the fall, switched to all B and stopped all purchases in Feb 17. I was ready to leave LC for good. All the credit for the model is Bryce Mason's, not mine, but I have a pretty good handle how it worked and my comments are based on that understanding. We collaborated on quite a number of things back then.

I'm sorry to be leaving LC as it's been both an interesting and profitable hobby.
Guess when it became less profitable it also became less interesting.