Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
Credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. Machine learning algorithm such as LightGBM can be used to evaluate credit risk. However, the results in ev...
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| Language: | en |
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2023
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| Online Access: | http://eprints.uthm.edu.my/9376/1/J15898_e63681e26a66ff10c518c7ea4a580069.pdf http://eprints.uthm.edu.my/9376/ http://doi.org/10.26594/register.v9i1.3060 |
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| author | Yosza Dasril a, Yosza Dasril a Muslim, Much Aziz Al Hakim, M. Faris Jumanto, Jumanto Budi Prasetiyo, Budi Prasetiyo |
| author_facet | Yosza Dasril a, Yosza Dasril a Muslim, Much Aziz Al Hakim, M. Faris Jumanto, Jumanto Budi Prasetiyo, Budi Prasetiyo |
| author_sort | Yosza Dasril a, Yosza Dasril a |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. Machine learning algorithm such as LightGBM
can be used to evaluate credit risk. However, the results in evaluating P2P lending need to be improved. This research aims to improve the accuracy of the LightGBM algorithm by combining it with the Particle Swarm Optimization (PSO) algorithm. This research is novel as it combines LightGBM
with PSO for large data from the Lending Club Dataset, which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% accuracy, 90.514% Recall, and 97.754% NPV, respectively. The combination of LightGBM and PSO has resulted in better outcome. |
| format | Article |
| id | my.uthm.eprints-9376 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | unipdu |
| record_format | eprints |
| spelling | my.uthm.eprints-93762023-07-30T07:09:57Z http://eprints.uthm.edu.my/9376/ Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Yosza Dasril a, Yosza Dasril a Muslim, Much Aziz Al Hakim, M. Faris Jumanto, Jumanto Budi Prasetiyo, Budi Prasetiyo T Technology (General) Credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. Machine learning algorithm such as LightGBM can be used to evaluate credit risk. However, the results in evaluating P2P lending need to be improved. This research aims to improve the accuracy of the LightGBM algorithm by combining it with the Particle Swarm Optimization (PSO) algorithm. This research is novel as it combines LightGBM with PSO for large data from the Lending Club Dataset, which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% accuracy, 90.514% Recall, and 97.754% NPV, respectively. The combination of LightGBM and PSO has resulted in better outcome. unipdu 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9376/1/J15898_e63681e26a66ff10c518c7ea4a580069.pdf Yosza Dasril a, Yosza Dasril a and Muslim, Much Aziz and Al Hakim, M. Faris and Jumanto, Jumanto and Budi Prasetiyo, Budi Prasetiyo (2023) Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization. Jurnal Ilmiah Teknologi Sistem Informasi, 9 (1). pp. 18-28. ISSN 2502-3357 ( http://doi.org/10.26594/register.v9i1.3060 |
| spellingShingle | T Technology (General) Yosza Dasril a, Yosza Dasril a Muslim, Much Aziz Al Hakim, M. Faris Jumanto, Jumanto Budi Prasetiyo, Budi Prasetiyo Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization |
| title | Credit Risk Assessment in P2P Lending Using LightGBM and
Particle Swarm Optimization |
| title_full | Credit Risk Assessment in P2P Lending Using LightGBM and
Particle Swarm Optimization |
| title_fullStr | Credit Risk Assessment in P2P Lending Using LightGBM and
Particle Swarm Optimization |
| title_full_unstemmed | Credit Risk Assessment in P2P Lending Using LightGBM and
Particle Swarm Optimization |
| title_short | Credit Risk Assessment in P2P Lending Using LightGBM and
Particle Swarm Optimization |
| title_sort | credit risk assessment in p2p lending using lightgbm and
particle swarm optimization |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9376/1/J15898_e63681e26a66ff10c518c7ea4a580069.pdf http://eprints.uthm.edu.my/9376/ http://doi.org/10.26594/register.v9i1.3060 |
| url_provider | http://eprints.uthm.edu.my/ |