Predicting Credit Risk in European P2P Lending: A Case Study of “Bondora” Using Supervised Machine Learning Techniques

Peer-to-Peer (P2P) lending has emerged as an attractive investment option for higher returns, contrasting with conventional bank deposits. However, the inherent risk of loan and interest non-repayment necessitates a thorough understanding of credit risk factors. This research delves into the credit risk analysis of one of Europe's prominent P2P lending platforms, “Bondora.” Leveraging publicly available loan application data, supervised machine learning techniques are employed to investigate and identify pivotal factors affecting credit risk. The study evaluates the performance of cutting-edge machine learning models, including Random Forest, Logistic Regression, XGBoost, Regularized Logistic Regression, Decision Tree, and Naïve Bayes, in predicting credit risk within the “Bondora” platform. Notably, XGBoost emerges as the most effective model, boasting the highest accuracy and AUC in loan default prediction. Additionally, Random Forest showcases exceptional recall in detecting default instances. Through this comprehensive analysis, the research facilitates the selection of the most suitable credit risk prediction method for P2P lending networks like “Bondora.” The study's insights empower investors to make informed decisions when considering future investments in such platforms, aiding in the management of potential risks and optimizing investment strategies.