Systemic Risk Measures and Machine Learning Algorithms in Islamic and Conventional Financial Institutions
Due to global economic volatility, financial institution risk management has become a major concern since risk expands due to the inherent interconnectedness within the sector, impacting stability and credit supply. Using machine learning as a forecasting instrument, the study examined systemic risk...
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/95592 |
| _version_ | 1848766032517267456 |
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| author | Sajjad, Shakeel |
| author_facet | Sajjad, Shakeel |
| author_sort | Sajjad, Shakeel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Due to global economic volatility, financial institution risk management has become a major concern since risk expands due to the inherent interconnectedness within the sector, impacting stability and credit supply. Using machine learning as a forecasting instrument, the study examined systemic risk in GCC and ASEAN in Islamic and conventional financial institutions. The study highlighted machine learning's potential to forecast outcomes accurately; and how to adjust regulatory policies to mitigate systemic events. |
| first_indexed | 2025-11-14T11:44:42Z |
| format | Thesis |
| id | curtin-20.500.11937-95592 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:44:42Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-955922024-08-06T00:41:22Z Systemic Risk Measures and Machine Learning Algorithms in Islamic and Conventional Financial Institutions Sajjad, Shakeel Due to global economic volatility, financial institution risk management has become a major concern since risk expands due to the inherent interconnectedness within the sector, impacting stability and credit supply. Using machine learning as a forecasting instrument, the study examined systemic risk in GCC and ASEAN in Islamic and conventional financial institutions. The study highlighted machine learning's potential to forecast outcomes accurately; and how to adjust regulatory policies to mitigate systemic events. 2024 Thesis http://hdl.handle.net/20.500.11937/95592 Curtin University restricted |
| spellingShingle | Sajjad, Shakeel Systemic Risk Measures and Machine Learning Algorithms in Islamic and Conventional Financial Institutions |
| title | Systemic Risk Measures and Machine Learning Algorithms in Islamic and
Conventional Financial Institutions |
| title_full | Systemic Risk Measures and Machine Learning Algorithms in Islamic and
Conventional Financial Institutions |
| title_fullStr | Systemic Risk Measures and Machine Learning Algorithms in Islamic and
Conventional Financial Institutions |
| title_full_unstemmed | Systemic Risk Measures and Machine Learning Algorithms in Islamic and
Conventional Financial Institutions |
| title_short | Systemic Risk Measures and Machine Learning Algorithms in Islamic and
Conventional Financial Institutions |
| title_sort | systemic risk measures and machine learning algorithms in islamic and
conventional financial institutions |
| url | http://hdl.handle.net/20.500.11937/95592 |