Moyo, S.Mphephu, N.Tshauambea, Murendeni2021-12-102021-12-102021-06-18Tshauambea, 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>.http://hdl.handle.net/11602/1798MSc (Applied Mathematics)Department of Mathematics and Applied MathematicsPeer-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 score1 online resource (v, 52 leaves) : color illustrationsenUniversity of VendaMachine learningUCTDP2P lendingCreditCreditworthinessCredit riskCredit Scoring and information assymmetryDissertationTshauambea M. Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques. []. , 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1798Tshauambea, M. (2021). <i>Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques</i>. (). . Retrieved from http://hdl.handle.net/11602/1798Tshauambea, Murendeni. <i>"Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques."</i> ., , 2021. http://hdl.handle.net/11602/1798TY - Dissertation AU - Tshauambea, Murendeni AB - 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 DA - 2021-06-18 DB - ResearchSpace DP - Univen KW - Machine learning KW - P2P lending KW - Credit KW - Creditworthiness KW - Credit risk KW - Credit Scoring and information assymmetry LK - https://univendspace.univen.ac.za PY - 2021 T1 - Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques TI - Improved Peer-to-Peer Lending Credit Scoring Mechanism using Machine Learning Techniques UR - http://hdl.handle.net/11602/1798 ER -