  There has been an increased need for secondary means of credit evaluation by
both traditional banking organizations as well as peer-to-peer lending
entities. This is especially important in the present technological era where
sticking with strict primary credit histories doesn't help distinguish between
a 'good' and a 'bad' borrower, and ends up hurting both the individual borrower
as well as the investor as a whole. We utilized machine learning classification
and clustering algorithms to accurately predict a borrower's creditworthiness
while identifying specific secondary attributes that contribute to this score.
While extensive research has been done in predicting when a loan would be fully
paid, the area of feature selection for lending is relatively new. We achieved
65% F1 and 73% AUC on the LendingClub data while identifying key secondary
attributes.
