Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Abstract This study analyzed the characteristics of high-risk defaulters in the peer-to-peer (P2P) lending market using data from a European-based P2P lending platform, Bondora, from 2009 to 2021. Borrowers often participate in the P2P lending market, because they do not have access to credit through regular commercial banks for various reasons. Lenders participate in this market to diversify their portfolios and seek higher returns as compensation for the financial risk. Despite the transparency of transactions and the information provided by borrowers, lenders or investors bear the credit risk owing to asymmetric information. This study finds that financial variables, such as loan amount, interest rate, credit score, and existing liabilities, are predictors of the probability of default. In contrast, demographic and cultural characteristics, such as age, gender, employment status, education level, and language, have varying effects on default. For instance, employment status and language do not significantly impact default.
Recommended Citation
Liu, Yajing and Mona, Kyoko, "Characteristics of High-Risk Defaulters: An Empirical Study on the Bondora P2P Lending Platform" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/sj/blockchain/3
Characteristics of High-Risk Defaulters: An Empirical Study on the Bondora P2P Lending Platform
Hilton Waikoloa Village, Hawaii
Abstract This study analyzed the characteristics of high-risk defaulters in the peer-to-peer (P2P) lending market using data from a European-based P2P lending platform, Bondora, from 2009 to 2021. Borrowers often participate in the P2P lending market, because they do not have access to credit through regular commercial banks for various reasons. Lenders participate in this market to diversify their portfolios and seek higher returns as compensation for the financial risk. Despite the transparency of transactions and the information provided by borrowers, lenders or investors bear the credit risk owing to asymmetric information. This study finds that financial variables, such as loan amount, interest rate, credit score, and existing liabilities, are predictors of the probability of default. In contrast, demographic and cultural characteristics, such as age, gender, employment status, education level, and language, have varying effects on default. For instance, employment status and language do not significantly impact default.
https://aisel.aisnet.org/hicss-58/sj/blockchain/3