Determining Secondary Attributes for Credit Evaluation in P2P Lending

TL;DR

This paper explores the use of machine learning to identify secondary attributes that improve credit evaluation accuracy in P2P lending, addressing limitations of primary credit histories.

Contribution

It introduces a novel approach to feature selection for credit scoring using classification and clustering algorithms in the P2P lending context.

Findings

Achieved 65% F1 score on LendingClub data

Achieved 73% AUC on LendingClub data

Identified key secondary attributes influencing creditworthiness

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.