Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning
Banks and financial institutions can avoid bankruptcy by following strict risk management techniques, diversifying their investment portfolios, keeping appropriate capital reserves, and doing thorough credit evaluations. Machine learning (ML) may help in bankruptcy prediction by analyzing massive qu...
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Bilijipub publisher
2024-06-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
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| Online Access: | https://jaism.bilijipub.com/article_199126_e242d74d45e783935bf40dc107afce6f.pdf |
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| author | Mohd Naved Ravi Kumar Shaiku Saheb |
| author_facet | Mohd Naved Ravi Kumar Shaiku Saheb |
| author_sort | Mohd Naved |
| collection | DOAJ |
| description | Banks and financial institutions can avoid bankruptcy by following strict risk management techniques, diversifying their investment portfolios, keeping appropriate capital reserves, and doing thorough credit evaluations. Machine learning (ML) may help in bankruptcy prediction by analyzing massive quantities of historical financial data, identifying trends and anomalies that indicate trouble, and developing predictive models to estimate the possibility of default. These models can use factors including liquidity ratios, debt levels, market circumstances, and economic indicators to offer early warnings of possible financial instabilities, allowing organizations to take proactive steps to reduce risk and avert bankruptcy. This paper endeavors to forecast bankruptcies by leveraging ML models. Specifically, the selected model, Histogram Gradient Boosting Classification (HGBC), is enriched through the integration of the Snake Optimization Algorithm (SOA), Gradient-Based Optimization (GBO), and Bonobo Optimization Algorithm (BOA). This amalgamation results in the creation of innovative hybrid models meticulously engineered to enhance the accuracy of bankruptcy predictions. The findings reveal that in scenarios of financial distress, the HGBC model exhibits the least efficacy, achieving a precision value of 0.940. Conversely, the HGBO and HGGB models demonstrate precision values of 0.950 and 0.960, respectively, showcasing marginally weaker performances compared to the HGSO model, which attains a remarkable precision value of 0.980. The proactive measures undertaken by banks and financial institutions, such as stringent risk management protocols and diversified investment strategies, play an important role in averting the specter of bankruptcy. |
| format | Article |
| id | doaj-art-11799f30711e44ed86f27f45e2c07d0a |
| institution | Kabale University |
| issn | 3041-850X |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Bilijipub publisher |
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| series | Journal of Artificial Intelligence and System Modelling |
| spelling | doaj-art-11799f30711e44ed86f27f45e2c07d0a2025-08-20T04:01:48ZengBilijipub publisherJournal of Artificial Intelligence and System Modelling3041-850X2024-06-010202183510.22034/jaism.2024.457068.1039199126Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine LearningMohd Naved0Ravi Kumar1Shaiku Saheb2Jaipuria Institute of Management, Noida, Uttar Pradesh, 201309, IndiaPG Department of Business Administration and Dean R&D Cell, Maris Stella College, Vijayawada, Andhra Pradesh, 520008, IndiaSchool of Business, VIT-AP University, Andhra Pradesh, 522241, IndiaBanks and financial institutions can avoid bankruptcy by following strict risk management techniques, diversifying their investment portfolios, keeping appropriate capital reserves, and doing thorough credit evaluations. Machine learning (ML) may help in bankruptcy prediction by analyzing massive quantities of historical financial data, identifying trends and anomalies that indicate trouble, and developing predictive models to estimate the possibility of default. These models can use factors including liquidity ratios, debt levels, market circumstances, and economic indicators to offer early warnings of possible financial instabilities, allowing organizations to take proactive steps to reduce risk and avert bankruptcy. This paper endeavors to forecast bankruptcies by leveraging ML models. Specifically, the selected model, Histogram Gradient Boosting Classification (HGBC), is enriched through the integration of the Snake Optimization Algorithm (SOA), Gradient-Based Optimization (GBO), and Bonobo Optimization Algorithm (BOA). This amalgamation results in the creation of innovative hybrid models meticulously engineered to enhance the accuracy of bankruptcy predictions. The findings reveal that in scenarios of financial distress, the HGBC model exhibits the least efficacy, achieving a precision value of 0.940. Conversely, the HGBO and HGGB models demonstrate precision values of 0.950 and 0.960, respectively, showcasing marginally weaker performances compared to the HGSO model, which attains a remarkable precision value of 0.980. The proactive measures undertaken by banks and financial institutions, such as stringent risk management protocols and diversified investment strategies, play an important role in averting the specter of bankruptcy.https://jaism.bilijipub.com/article_199126_e242d74d45e783935bf40dc107afce6f.pdfbankruptcyhistogram gradient boosting classificationsnake optimization algorithmgradient-based optimizationbonobo optimization algorithm |
| spellingShingle | Mohd Naved Ravi Kumar Shaiku Saheb Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning Journal of Artificial Intelligence and System Modelling bankruptcy histogram gradient boosting classification snake optimization algorithm gradient-based optimization bonobo optimization algorithm |
| title | Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning |
| title_full | Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning |
| title_fullStr | Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning |
| title_full_unstemmed | Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning |
| title_short | Analyzing Financial Stability by Predicting Bankruptcy Situations with Machine Learning |
| title_sort | analyzing financial stability by predicting bankruptcy situations with machine learning |
| topic | bankruptcy histogram gradient boosting classification snake optimization algorithm gradient-based optimization bonobo optimization algorithm |
| url | https://jaism.bilijipub.com/article_199126_e242d74d45e783935bf40dc107afce6f.pdf |
| work_keys_str_mv | AT mohdnaved analyzingfinancialstabilitybypredictingbankruptcysituationswithmachinelearning AT ravikumar analyzingfinancialstabilitybypredictingbankruptcysituationswithmachinelearning AT shaikusaheb analyzingfinancialstabilitybypredictingbankruptcysituationswithmachinelearning |