From Lending Club’s website:
“We are the world’s largest online credit marketplace, facilitating personal loans, business loans, and financing for elective medical procedures. Borrowers access lower interest rate loans through a fast and easy online or mobile interface. Investors provide the capital to enable many of the loans in exchange for earning interest. We operate fully online with no branch infrastructure, and use technology to lower cost and deliver an amazing experience. We pass the cost savings to borrowers in the form of lower rates and investors in the form of attractive returns. We’re transforming the banking system into a frictionless, transparent and highly efficient online marketplace, helping people achieve their financial goals everyday.”
Data from Lending Club can be downloaded directly from the company’s website at:
In this assignment, we will make use of a sample of data that contains information on the status of loans that were approved. Our objective is to identify those characteristics that are associated with a borrower being late or defaulting on their loan. For the purposes of the assignment, loans have been classified into two types. Loans classified as “Risky” include loans for which payments are overdue, loans that have been charged off and loans on which the borrower has default. All other loans are classified as “On Time.”
In addition to data on more than 800,000 loans, a summary of available predictors is included in the Excel document. Your team is tasked with building a model to identify the characteristics of loans that are likely to be “risky.” You may use any or all of the predictors included in the dataset. You may also consider transformations of the predictors or interactions among the predictors. In conducting your analysis, you should set aside a random sample of loans to evaluate the accuracy of your model in a holdout sample. For your convenience, the dataset is arranged in a random order, so you can use the last X% of loans in the dataset as a holdout sample.
Your deliverable should include:
The Excel file that contains (1) the spreadsheet on which you built your final model and (2) a worksheet that enables a user to input a set of values for predictors and receive the probability of the loan being classified as “risky.”
You should also submit a document that contains a discussion of the alternative models that you considered and how you settled on the final model. You may find it easier to provide a table that lists the predictors in your models and the metrics used to assess each model’s performance.
