Credit Risk Detection in Peer-to-Peer Lending Using CatBoost

Abstract

P2P (Peer-to-peer) lending has gained popularity among private borrowers, small businesses, and MSMEs due to its ability to provide direct access to loans without the strict requirements imposed by traditional banks and financial institutions. However, P2P lending faces a significant challenge in terms of credit risk, resulting in a high rate of loan repayment failures. To address this issue, the study aimed to develop a credit risk detection system using a loan dataset obtained from the Bondora company by implementing one of the gradient boosting algorithms which are called the CatBoost (Categorical Boosting) method. The performance of the CatBoost algorithm was evaluated using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve). Three scenarios were considered, and the results revealed that scenario 2, with a data splitting ratio of 90:10, achieved the best outcome with an AUC value of 0.80329. This outperformed scenario 1, with a data splitting ratio of 80:20 and an AUC value of approximately 0.789583, as well as scenario 3, with a data splitting ratio of 70:30 and an AUC value of around 0.781066.