Prediction of Default Risk in Peer-to-Peer Lending Using Structured and Unstructured Data

Autor/in	Lee, Jei Young
Titel	Prediction of Default Risk in Peer-to-Peer Lending Using Structured and Unstructured Data
Quelle	In: Journal of Financial Counseling and Planning, 31 (2020) 1, S.115-129 (15 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Lee, Jei Young)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1052-3073
Schlagwörter	Money Management; Loan Default; Loan Repayment; Correlation; Credit (Finance); Finance Occupations; Counselors; Data Analysis; Risk; Models; Corporations + Suchen Sie Ihr Suchwort? Schuldnerverzug; Korrelation; Credit; Kredit; Finanzbuchhalter; Counselor; Counsellor; Counsellors; Berater; Auswertung; Risiko; Analogiemodell; Unternehmen
Abstract	Using data from Lending Club, we analyzed funded loans between 2012 and 2013, the default status of which were mostly known in 2018. Our results showed that both the borrower characteristics and the conditions of the loan were significantly associated with the loan default rate. Results also showed that the sentiment of a user-written loan description influenced the borrower's loan interest rates. It contributes to expanding the scope of peer-to-peer (P2P) loan research by implementing unstructured data as a new model variable. Financial counselors need to consider the growth potential of the P2P loan market using data analysis: This will reveal niche market opportunities, enabling the development of services necessary for the safe supply of small loans at reasonable interest rates. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01