Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms
AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakist...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/6858916 |
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| _version_ | 1832562070055288832 |
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| author | Shamsa Khalid Muhammad Anees Khan M.S. Mazliham Muhammad Mansoor Alam Nida Aman Muhammad Tanvir Taj Rija Zaka Muhammad Jehangir |
| author_facet | Shamsa Khalid Muhammad Anees Khan M.S. Mazliham Muhammad Mansoor Alam Nida Aman Muhammad Tanvir Taj Rija Zaka Muhammad Jehangir |
| author_sort | Shamsa Khalid |
| collection | DOAJ |
| description | AI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques. |
| format | Article |
| id | doaj-art-ae5f2af30afb498b83f6d3502b85b526 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ae5f2af30afb498b83f6d3502b85b5262025-02-03T01:23:35ZengWileyComplexity1099-05262022-01-01202210.1155/2022/6858916Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial FirmsShamsa Khalid0Muhammad Anees Khan1M.S. Mazliham2Muhammad Mansoor Alam3Nida Aman4Muhammad Tanvir Taj5Rija Zaka6Muhammad Jehangir7Master in Finance ScholarDepartment of Management Studies Bahria UniversityFaculty of Computing and InformaticsFaculty of Computing and InformaticsDepartment of Management Studies Bahria UniversityDepartment of Management Studies Bahria UniversityDepartment of Management Studies Bahria UniversityAbdul Wali Khan University MardanAI (artificial intelligence) is a significant technological advancement that has everyone buzzing about its incredible potential. The current research study evaluates the influence of supervised artificial intelligence techniques, i.e., machine learning techniques on the nonfinancial firms of Pakistan and focuses on the practical application of AI techniques for the accurate prediction of corporate risks which in turn will lead to the automation of corporate risk management. So, in this study, we used financial ratios for accurate risk assessment and for the automation of corporate risk management by developing machine learning algorithms using techniques, namely, random forest, decision tree, naïve Bayes, and KNN. A secondary data collection technique will be used. For this purpose, we collected annual data of nonfinancial companies in Pakistan for the period ranging from 2006 to 2020, and the data are analyzed and tested through Python software. Our results prove that AI techniques can accurately predict risk with minimum error values, and among all the techniques used, the random forest technique outperforms as compared to the rest of the techniques.http://dx.doi.org/10.1155/2022/6858916 |
| spellingShingle | Shamsa Khalid Muhammad Anees Khan M.S. Mazliham Muhammad Mansoor Alam Nida Aman Muhammad Tanvir Taj Rija Zaka Muhammad Jehangir Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms Complexity |
| title | Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms |
| title_full | Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms |
| title_fullStr | Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms |
| title_full_unstemmed | Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms |
| title_short | Predicting Risk through Artificial Intelligence Based on Machine Learning Algorithms: A Case of Pakistani Nonfinancial Firms |
| title_sort | predicting risk through artificial intelligence based on machine learning algorithms a case of pakistani nonfinancial firms |
| url | http://dx.doi.org/10.1155/2022/6858916 |
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