Evaluating Borrowers’ Default Risk in Peer-To-Peer Lending: Evidence from A Lending Platform in Estonia (Europe)
DOI:
https://doi.org/10.61424/rjbe.v3i3.502Keywords:
Peer-to-peer (P2P) lending, binary logistic regression, loan default, risk assessmentAbstract
This study uses data from the Estonian Bondora platform to examine the factors that influence borrower default risk in peer-to-peer (P2P) lending. To investigate the impact of personal traits, loan qualities, and financial factors on default probability, 103,326 loan requests from March 2009 to June 2022 were examined. The results of binary logistic regression demonstrate that while debt-to-income ratio and length of employment did not significantly affect loan default, age, gender, marital status, education level, loan amount, monthly payment, work experience, and interest rate do. While single and divorced borrowers were at a higher risk, married and female borrowers had lower default rates. Reduced default chance was linked to both stable income levels and higher levels of education. These results provide P2P lending platforms with valuable insights to inform loan issuance and borrowers to make informed decisions that enhance both parties’ assessment of credit risk.
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