
Predicting Lending Club Loan Defaults and Using Them to Maximize Returns - TheDataInc
https://isaac-thedataincubator-project.herokuapp.com/
======
7Figures2Commas
Minimizing defaults during a period of low defaults is a different exercise
than protecting a portfolio against defaults period. Doing the latter is
difficult with alternative lenders because the data doesn't go back far enough
to be meaningful (most of the loan volume knows only the good part of the
economic cycle).

I was in a meeting at a bank recently and the topic of alternative lenders
came up. The bank's chief credit officer limited his comments to "I'd like to
see how their portfolios and models look after the next economic downturn."
Wise words.

~~~
tinco
How did your banks portfolios and models look after the previous one? It's
easy to speak 'wisely' like this when the previous economic down turn was just
a few years ago, but if recent history is any predictor the banks will have
exactly the same naieve portfolios and models 10 years from now.

Anyway, this page seems to have data from 2007 and on, so I wouldn't really
call that a 'low default' period..

~~~
7Figures2Commas
It's a common mistake to refer to "the banks" as if they are all the same when
they are not. The bank in question is a community bank with a conservative
loan portfolio. It did not experience the same losses banks with more
aggressive portfolios did.

As for the data you reference, in all of 2007, Lending Club did about $6
million in loans[1]. For comparison, it did over a billion dollars in loans in
Q3 2014. Again, alternative online lenders have built the vast majority of
their portfolios (or originated most of their loans in the case of services
like Lending Club) during an unprecedented period of extraordinarily low
interest rates and historically low defaults.

I'm not suggesting that all of these loans are going to go bad when the
economy turns; I was simply pointing out that exercises like the one linked to
here are quite limited in their real-world utility.

[1]
[https://www.lendingclub.com/info/statistics.action](https://www.lendingclub.com/info/statistics.action)

------
toasted
Has anyone considered the grameen model of peer pressure lending in P2p
marketplaces?

You signup with your facebook account, it records all the details of your
facebook friends, and if you go into default on your loan for more than 3
payments then it starts contacting your facebook friends automatically and
stating that unfortunately you were unable to keep up with your debts and
could your friends chip in some money to help cover things??

the threat of losing face would probably decrease default rates significantly,
therefore allowing a decreased interest rate to be charged.

------
klochner
light on details or analysis

~~~
baldeagle
There is a github behind it. It has an ipython notebook, but I haven't opened
it up to check out if there is enough there for reproducible research.
[https://github.com/tianhuil/isaac-thedataincubator-
project](https://github.com/tianhuil/isaac-thedataincubator-project)

~~~
mLewisLogic
The data files (data/LoanStats3a.csv) are missing.

Downloadable from Lending Club though:
[https://www.lendingclub.com/info/download-
data.action](https://www.lendingclub.com/info/download-data.action)

------
rubyfan
yhat had a nice article on this exact subject and data set with a good walk
through of the R code.

[http://blog.yhathq.com/posts/machine-learning-for-
predicting...](http://blog.yhathq.com/posts/machine-learning-for-predicting-
bad-loans.html)

