Determining Secondary Attributes for Credit Evaluation in P2P Lending

• URL: http://arxiv.org/abs/2006.13921v1
• Date: Mon, 8 Jun 2020 16:12:00 GMT
• Title: Determining Secondary Attributes for Credit Evaluation in P2P Lending
• Authors: Revathi Bhuvaneswari, Antonio Segalini
• Abstract summary: We utilize machine learning classification and clustering algorithms to accurately predict a borrower's creditworthiness. We achieved 65% F1 and 73% AUC on the LendingClub data while identifying key secondary attributes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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