Credit Risk Detection in Peer-to-Peer Lending Using CatBoost
Abstract
P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk to minimize the risk of P2P lending. In this study, a system had been built using the CatBoost method; the dataset used was taken from the Bondora loan dataset. To measure the performance of the CatBoost algorithm, an evaluation matrix was performed using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) was performed. The experiment consists of three scenarios, of which the best result regards Scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value 0.80329 compared to scenario 1 with a data split of 80:20 with the AUC value around 0.789583, and scenario 3 with a data split of 70:30 with the AUC value around 0.781066, respectively.
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References
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