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- W3037655659 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." @default.
- W3037655659 created "2020-07-02" @default.
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- W3037655659 date "2020-06-08" @default.
- W3037655659 modified "2023-09-27" @default.
- W3037655659 title "Determining Secondary Attributes for Credit Evaluation in P2P Lending." @default.
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