Lending Club
Visit Lending Club (Links to an external site.) which provides data about loan applications it has rejected as well as the performance of loans that it issued. Locate 2018 Q3 datafor the Loan Database and the Declined loan data.
Locate the Lending Club data dictionary for the loans that were approved and funded. Note all of the data attributes listed in the Excel files (csv) as fields,
- Which attributes do you think might predict which loans will go delinquent and which will ultimately be fully repaid?
- How could we test that?
Now consider the declined loans data set of LendingClub for Q3 2018.
- 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? After reviewing your classmate’s responses, do you agree or disagree with their position? Did their rationale change your mind?
Adapted from Data Analytics for Accounting (Richardson, et al., 2018)
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This is a discussion board post for a senior level accounting course, please include TWO APA cited and referenced peer reviewed sources with the link on the reference page, will not need a cover sheet, thank you.
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