Classification with inputs that change over time – P2P Loan Data

Predicting whether a loan will default or not is a tricky task. It may involve many variables, incomplete information and is a task that involves time as a component. Loans may also perform for a while before they default. Some loans may even be late, but recover back to the regular payment schedule. It’s an interesting application for statistics.

The LendingClub website, a service offering peer-to-peer lending, offers an interesting data set: historical data of loan performance as well as data for new loans. I’ve been playing around a bit with the data and built a model to predict whether a loan is a good investment. The LendingClub data is available for download. A data dictionary can be found on the website also.

First we need to define the outcome we want to predict. A loan can be in several states, some being “current”, others being “defaulted”, “late” or even on a “performing payment plan”. Conservatively, I defined all loans that were not “paid off” as bad. Loans that are “current” were excluded as they still can default in the future. Loans that are “late” are considered bad, because the borrower run into problems. The model I’m trying to built is basically for a conservative investor looking for loans that will simply be paid back without a hitch. With the usual statistical techniques a model can be built and the performance can be measured by 10-fold cross-validation or evaluating the model on a hold-out set. The real result of a prediction will of course only be available after about 3 years when a loan is fully paid off. As measure to optimize I chose the AUC metric. A 10-fold cross-validation estimates the performance of my model at 0.698 which is not too bad. The predictions implicitly make a few assumptions. The first one being that future performance of loans will be similar to historical performance of similar loans. I’m assuming a stationary distribution and the IID assumption – which is not completely true in reality, but hopefully close enough 🙂 Also, inflation expectations were not taken into account, but I’m limiting my model to 36 month loans to make that more manageable.

I won’t go into the details of how I encoded the variables and what variables I’m using. I discovered that I can extract information out of the textual variables in the loans. The “Loan Description”, a free text field where potential borrowers can leave comments or answer questions, is quite predictive. The difficult part is using that information in practice. A loan is in “funding state” for two weeks were investors can ask questions and invest in the loan. Many loans get fully funded before the two week period is over, some without any question or comment on the loan. New information may become available in the Loan Description field that may change the classification. That means, however, that the prediction may change over time – positively or negatively – after an investment decision has been. Not ideal, but the variables are quite powerful so I’m still looking for a good solution.

I made the ratings for the LendingClub loans my program produces public. I will update them occasionally (i.e., whenever I feel like it). If you have some suggestions on how to use the textual variables, leave a comment.

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