Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction
In the dynamic landscape of financial risk management, accurately predicting banks' susceptibility to bankruptcy is deemed a critical imperative. Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classificat...
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| Language: | English |
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Bilijipub publisher
2024-03-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
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| Online Access: | https://jaism.bilijipub.com/article_193320_992713a8dfcb5042cdd01e83a191879a.pdf |
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| author | Yupeng Li Ke Sun |
| author_facet | Yupeng Li Ke Sun |
| author_sort | Yupeng Li |
| collection | DOAJ |
| description | In the dynamic landscape of financial risk management, accurately predicting banks' susceptibility to bankruptcy is deemed a critical imperative. Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classification model (RFC), The Grasshopper Optimizer Algorithm (GOA), and the Artificial Rabbits Optimizer (ARO). The intricate interplay of these techniques aims to enhance the precision and efficacy of bankruptcy prediction models. Traditional methodologies often fall short of capturing the complexity of contemporary financial landscapes. Machine learning provides an avenue to address this challenge, simultaneously allowing for a nuanced analysis of various factors. This research responds to the existing literature gap by employing data mining techniques for automated knowledge extraction from financial databases and emphasizing a pioneering approach grounded in the qualitative domain. Specifically, superior performance is exhibited by the combined model, denoted as RFAR, with an accuracy of 0.972, in stark contrast to the RFGO model, which achieves an accuracy of 0.956, and the RFC model, identified as the weakest among these models, with an accuracy of 0.930. This notable difference underscores the effectiveness of integrating machine learning methodologies, particularly the RFC model, GOA, and ARO, in predicting the likelihood of banks going bankrupt. |
| format | Article |
| id | doaj-art-6ab6142ef5a44e49804b960dea6f5b77 |
| institution | Kabale University |
| issn | 3041-850X |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Journal of Artificial Intelligence and System Modelling |
| spelling | doaj-art-6ab6142ef5a44e49804b960dea6f5b772025-08-20T03:36:53ZengBilijipub publisherJournal of Artificial Intelligence and System Modelling3041-850X2024-03-0102019911310.22034/jaism.2024.445968.1029193320Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk PredictionYupeng Li0Ke Sun1School of Accountancy Anyang Institute of Technology, 455000, Henan, ChinaUBS Business Solutions (China) Co. Ltd Wuxi Branch, Wuxi, Jiangsu, 214000, ChinaIn the dynamic landscape of financial risk management, accurately predicting banks' susceptibility to bankruptcy is deemed a critical imperative. Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classification model (RFC), The Grasshopper Optimizer Algorithm (GOA), and the Artificial Rabbits Optimizer (ARO). The intricate interplay of these techniques aims to enhance the precision and efficacy of bankruptcy prediction models. Traditional methodologies often fall short of capturing the complexity of contemporary financial landscapes. Machine learning provides an avenue to address this challenge, simultaneously allowing for a nuanced analysis of various factors. This research responds to the existing literature gap by employing data mining techniques for automated knowledge extraction from financial databases and emphasizing a pioneering approach grounded in the qualitative domain. Specifically, superior performance is exhibited by the combined model, denoted as RFAR, with an accuracy of 0.972, in stark contrast to the RFGO model, which achieves an accuracy of 0.956, and the RFC model, identified as the weakest among these models, with an accuracy of 0.930. This notable difference underscores the effectiveness of integrating machine learning methodologies, particularly the RFC model, GOA, and ARO, in predicting the likelihood of banks going bankrupt.https://jaism.bilijipub.com/article_193320_992713a8dfcb5042cdd01e83a191879a.pdfartificial rabbits optimizerrandom forest classification modelgrasshopper optimizer algorithmmachine learningbankruptcy |
| spellingShingle | Yupeng Li Ke Sun Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction Journal of Artificial Intelligence and System Modelling artificial rabbits optimizer random forest classification model grasshopper optimizer algorithm machine learning bankruptcy |
| title | Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction |
| title_full | Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction |
| title_fullStr | Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction |
| title_full_unstemmed | Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction |
| title_short | Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction |
| title_sort | unleashing predicting a comparative study of machine learning for bankruptcy risk prediction |
| topic | artificial rabbits optimizer random forest classification model grasshopper optimizer algorithm machine learning bankruptcy |
| url | https://jaism.bilijipub.com/article_193320_992713a8dfcb5042cdd01e83a191879a.pdf |
| work_keys_str_mv | AT yupengli unleashingpredictingacomparativestudyofmachinelearningforbankruptcyriskprediction AT kesun unleashingpredictingacomparativestudyofmachinelearningforbankruptcyriskprediction |