Determining Secondary Attributes for Credit Evaluation in P2P Lending
This addresses the need for improved credit evaluation methods for lenders and borrowers, but it is incremental as it builds on existing prediction research with a focus on feature selection.
The paper tackled the problem of distinguishing between good and bad borrowers in peer-to-peer lending by using machine learning to predict creditworthiness and identify secondary attributes, achieving 65% F1 and 73% AUC on LendingClub data.
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.