Question

• What three items do you believe would be most useful in predicting loan acceptance or rejection?
• What additional data do you think could be solicited either internally or externally that would help you predict loan acceptance or rejection?
• If you were in a position to accept or deny a loan application, how might you look differently at this data? Would you be more lenient or stringent?

Answer 1

The two most critical questions in the lending industry are: 1) How risky is the borrower? 2) Given the borrower’s risk, should we lend him/her? The answer to the first question determines the interest rate the borrower would have. Interest rate measures among other things (such as time value of money) the riskness of the borrower, i.e. the riskier the borrower, the higher the interest rate. With interest rate in mind, we can then determine if the borrower is eligible for the loan.

Investors (lenders) provide loans to borrowers in exchange for the promise of repayment with interest. That means the lender only makes profit (interest) if the borrower pays off the loan. However, if he/she doesn’t repay the loan, then the lender loses money.

We’ll be using publicly available data from LendingClub.com. The data covers the 9,578 loans funded by the platform between May 2007 and February 2010. The interest rate is provided to us for each borrower. Therefore, so we’ll address the second question indirectly by trying to predict if the borrower will repay the loan by its mature date or not. Through this excerise we’ll illustrate three modeling concepts:

• What to do with missing values.
• Techniques used with imbalanced classification problems.
• Illustrate how to build an ensemble model using two methods: blending and stacking, which most likely gives us a boost in performance.

Below is a short description of each feature in the data set:

• credit_policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.
• purpose: The purpose of the loan such as: credit_card, debt_consolidation, etc.
• int_rate: The interest rate of the loan (proportion).
• installment: The monthly installments ($) owed by the borrower if the loan is funded.
• log_annual_inc: The natural log of the annual income of the borrower.
• dti: The debt-to-income ratio of the borrower.
• fico: The FICO credit score of the borrower.
• days_with_cr_line: The number of days the borrower has had a credit line.
• revol_bal: The borrower’s revolving balance.
• revol_util: The borrower’s revolving line utilization rate.
• inq_last_6mths: The borrower’s number of inquiries by creditors in the last 6 months.
• delinq_2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.
• pub_rec: The borrower’s number of derogatory public records.
• not_fully_paid: indicates whether the loan was not paid back in full (the borrower either defaulted or the borrower was deemed unlikely to pay it back).