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.

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