Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.
Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to enhance bank risk identification within this cont...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327199 |
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| author | Jiaqing Huang Yang Liu Miaomiao Tu Osama Sohaib |
| author_facet | Jiaqing Huang Yang Liu Miaomiao Tu Osama Sohaib |
| author_sort | Jiaqing Huang |
| collection | DOAJ |
| description | Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to enhance bank risk identification within this context. The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. Experiments were conducted on three public datasets: Bank Marketing, Lending Club, and German Credit. Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). These findings demonstrate the model's effectiveness and practical value in dynamic international trade scenarios, offering a reliable approach for enhanced bank credit risk evaluation. |
| format | Article |
| id | doaj-art-dcf426fbc60f47fd98ada6e8d7c86ab3 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-dcf426fbc60f47fd98ada6e8d7c86ab32025-08-20T03:49:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032719910.1371/journal.pone.0327199Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.Jiaqing HuangYang LiuMiaomiao TuOsama SohaibAccurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to enhance bank risk identification within this context. The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. Experiments were conducted on three public datasets: Bank Marketing, Lending Club, and German Credit. Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). These findings demonstrate the model's effectiveness and practical value in dynamic international trade scenarios, offering a reliable approach for enhanced bank credit risk evaluation.https://doi.org/10.1371/journal.pone.0327199 |
| spellingShingle | Jiaqing Huang Yang Liu Miaomiao Tu Osama Sohaib Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. PLoS ONE |
| title | Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. |
| title_full | Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. |
| title_fullStr | Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. |
| title_full_unstemmed | Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. |
| title_short | Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. |
| title_sort | design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network |
| url | https://doi.org/10.1371/journal.pone.0327199 |
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