Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks
Loan risk evaluation is critical for the safety and expansion of financial institutions, but it poses substantial hurdles owing to the intricacy of the data involved. This paper provides an innovative computational approach, the Particle Swarm Optimization-Excited Binary Grey Wolf Optimization-CatB...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
International Information and Engineering Technology Association
2024
|
Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/46892/1/ijsse_14.04_29.pdf http://ir.unimas.my/id/eprint/46892/ https://www.iieta.org/journals/ijsse/paper/10.18280/ijsse.140429 https://doi.org/10.18280/ijsse.140429 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.unimas.ir-46892 |
---|---|
record_format |
eprints |
spelling |
my.unimas.ir-468922024-12-13T00:18:56Z http://ir.unimas.my/id/eprint/46892/ Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks Suihai, Chen Bong, Chih How Chiu, Po Chan QA75 Electronic computers. Computer science Loan risk evaluation is critical for the safety and expansion of financial institutions, but it poses substantial hurdles owing to the intricacy of the data involved. This paper provides an innovative computational approach, the Particle Swarm Optimization-Excited Binary Grey Wolf Optimization-CatBoost (PSO-EBGWO-CatBoost) method, which is intended to improve loan risk forecast accuracy. The proposed framework uses PSO for optimum feature selection, while EBGWO fine-tunes CatBoost's hyperparameters, resulting in better predictive efficiency. Before using the PSO-EBGWO-CatBoost model, the input dataset is preprocessed to remove outliers and missing values. The model's efficiency was verified using a loan dataset, and the findings showed outstanding results in loan risk estimate, with an accuracy of 81.23%, precision of 82.10%, and recall of 80.26%. These findings show that the suggested method greatly outperforms existing strategies, making it an effective instrument for loan risk handling in financial organizations. International Information and Engineering Technology Association 2024-08-04 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46892/1/ijsse_14.04_29.pdf Suihai, Chen and Bong, Chih How and Chiu, Po Chan (2024) Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks. International Journal of Safety and Security Engineering, 14 (4). pp. 1331-1337. ISSN 2041-904X https://www.iieta.org/journals/ijsse/paper/10.18280/ijsse.140429 https://doi.org/10.18280/ijsse.140429 |
institution |
Universiti Malaysia Sarawak |
building |
Centre for Academic Information Services (CAIS) |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Sarawak |
content_source |
UNIMAS Institutional Repository |
url_provider |
http://ir.unimas.my/ |
language |
English |
topic |
QA75 Electronic computers. Computer science |
spellingShingle |
QA75 Electronic computers. Computer science Suihai, Chen Bong, Chih How Chiu, Po Chan Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
description |
Loan risk evaluation is critical for the safety and expansion of financial institutions, but it poses substantial hurdles owing to the intricacy of the data involved. This paper provides an innovative computational approach, the Particle Swarm Optimization-Excited Binary
Grey Wolf Optimization-CatBoost (PSO-EBGWO-CatBoost) method, which is intended to improve loan risk forecast accuracy. The proposed framework uses PSO for optimum feature selection, while EBGWO fine-tunes CatBoost's hyperparameters, resulting in better predictive efficiency. Before using the PSO-EBGWO-CatBoost model, the input
dataset is preprocessed to remove outliers and missing values. The model's efficiency was verified using a loan dataset, and the findings showed outstanding results in loan risk estimate, with an accuracy of 81.23%, precision of 82.10%, and recall of 80.26%. These findings show that the suggested method greatly outperforms existing strategies, making it an effective instrument for loan risk handling in financial organizations. |
format |
Article |
author |
Suihai, Chen Bong, Chih How Chiu, Po Chan |
author_facet |
Suihai, Chen Bong, Chih How Chiu, Po Chan |
author_sort |
Suihai, Chen |
title |
Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
title_short |
Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
title_full |
Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
title_fullStr |
Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
title_full_unstemmed |
Innovative Computational PSO-EBGWO-CatBoost Approach for Assessing Loan Risks |
title_sort |
innovative computational pso-ebgwo-catboost approach for assessing loan risks |
publisher |
International Information and Engineering Technology Association |
publishDate |
2024 |
url |
http://ir.unimas.my/id/eprint/46892/1/ijsse_14.04_29.pdf http://ir.unimas.my/id/eprint/46892/ https://www.iieta.org/journals/ijsse/paper/10.18280/ijsse.140429 https://doi.org/10.18280/ijsse.140429 |
_version_ |
1818839392382353408 |
score |
13.222552 |