Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques

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dc.contributor.advisor Moyo, S.
dc.contributor.advisor Mphephu, N.
dc.contributor.author Tshauambea, Murendeni
dc.date 2021
dc.date.accessioned 2021-12-10T13:24:29Z
dc.date.available 2021-12-10T13:24:29Z
dc.date.issued 2021-06-18
dc.identifier.citation Tshauambea, M. (2021) Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques. University of Venda, South Africa.<http://hdl.handle.net/11602/1798>.
dc.identifier.uri http://hdl.handle.net/11602/1798
dc.description MSc (Applied Mathematics) en_ZA
dc.description Department of Mathematics and Applied Mathematics
dc.description.abstract Peer-to-Peer(P2P) financing is a fast developing modern financial exchange network, which bypasses conventional intermediaries by linking lenders and borrowers directly. However, the online P2P lending platforms are faced with a problem of information asymmetry between lenders and borrowers. Assessing borrower’s creditworthiness is important because many P2P loans are not secured by collateral. Banks use credit scoring to evaluate borrower’s creditworthiness and reduce potential loan default risk. However, in P2P lending platform effective credit scoring models are hard to build due to insufficient credit information. This work is based on an empirical study by using the public dataset from the LendingClub, one of the largest online P2P lending platform in the USA. The aim of this study is to investigate the influential factors on loan performance on the basis of the credit score in the online P2P lending industry. This work improves the online credit scoring models and gives insight into the specific determinants that are influential for the score en_ZA
dc.description.sponsorship NRF en_ZA
dc.format.extent 1 online resource (v, 52 leaves) : color illustrations
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject Machine learning en_ZA
dc.subject P2P lending en_ZA
dc.subject Credit en_ZA
dc.subject Creditworthiness en_ZA
dc.subject Credit risk en_ZA
dc.subject Credit Scoring and information assymmetry en_ZA
dc.title Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques en_ZA
dc.type Dissertation en_ZA

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