Credit Risk Detection in Peer-to-Peer Lending Using CatBoost
Dublin Core
Title
Credit Risk Detection in Peer-to-Peer Lending Using CatBoost
Subject
P2P lending; credit risk detection; catboost; AUC; ROC
Description
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
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
Creator
Fadhlurrahman Akbar Nasution, Siti Saadah, Prasti Eko Yunanto
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
October 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Fadhlurrahman Akbar Nasution, Siti Saadah, Prasti Eko Yunanto, “Credit Risk Detection in Peer-to-Peer Lending Using CatBoost,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10102.