Journal Article

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

Lee, Jei Young

2020

Overview

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.

• Publisher: Springer Publishing Company,Association for Financial Counseling and Planning Education
• Subject: Asymmetry / Behavioral Sciences / Corporations / Correlation / Counseling / Counselors / Credit (Finance) / Credit risk / Data Analysis / Default / Finance Occupations / Financial institutions / Gender differences / General Counseling / Home ownership / Interest rates / Loan Default / Loan Repayment / Loans / Marriage and Family Counselling / Money Management / Ownership / Peer to peer computing / Peer to peer lending / Probability / Researchers / Revolving credit / Risk / Risk Assessment / Risk exposure / Sentiment analysis / Studies / Variables