Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking
<p dir="ltr">This study utilizes the random forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger s...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513545024045056 |
|---|---|
| author | Ahmet Faruk Aysan (11902115) |
| author2 | Bekir Sait Ciftler (17541801) Ibrahim Musa Unal (19418800) |
| author2_role | author author |
| author_facet | Ahmet Faruk Aysan (11902115) Bekir Sait Ciftler (17541801) Ibrahim Musa Unal (19418800) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmet Faruk Aysan (11902115) Bekir Sait Ciftler (17541801) Ibrahim Musa Unal (19418800) |
| dc.date.none.fl_str_mv | 2024-03-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/jrfm17030104 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Predictive_Power_of_Random_Forests_in_Analyzing_Risk_Management_in_Islamic_Banking/29117123 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Banking, finance and investment Business systems in context Strategy, management and organisational behaviour Information and computing sciences Cybersecurity and privacy Philosophy and religious studies Religious studies Risk management Islamic banks Survey analysis Random forest Machine learning |
| dc.title.none.fl_str_mv | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This study utilizes the random forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger survey budgets. Moreover, a positive link is established between a country’s development, characterized by high GDPs and low inflation and interest rates, and the precision of Islamic banks’ survey responses. Analyzing risk-related concerns, the study notes a significant reduction in credit portfolio risk attributed to improved risk management practices, global economic growth, stricter regulations, and diversified asset portfolios. Concerns related to terrorism financing and cybersecurity risks have also decreased due to the better enforcement of anti-money laundering regulations and investments in cybersecurity infrastructure and education. This research enhances our understanding of risk management in Islamic banks, highlighting the impact of bank size and country development. Additionally, it emphasizes the need for ongoing analysis beyond 2021 to account for potential COVID-19 effects and evolving risk management and regulatory practices in Islamic banking.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Risk and Financial Management<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/jrfm17030104" target="_blank">https://dx.doi.org/10.3390/jrfm17030104</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ea44867abaf5236ebbb17644236c722f |
| identifier_str_mv | 10.3390/jrfm17030104 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29117123 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Predictive Power of Random Forests in Analyzing Risk Management in Islamic BankingAhmet Faruk Aysan (11902115)Bekir Sait Ciftler (17541801)Ibrahim Musa Unal (19418800)Commerce, management, tourism and servicesBanking, finance and investmentBusiness systems in contextStrategy, management and organisational behaviourInformation and computing sciencesCybersecurity and privacyPhilosophy and religious studiesReligious studiesRisk managementIslamic banksSurvey analysisRandom forestMachine learning<p dir="ltr">This study utilizes the random forest technique to investigate risk management practices and concerns in Islamic banks using survey data from 2016 to 2021. Findings reveal that larger banks provide more consistent survey responses, driven by their confidence and larger survey budgets. Moreover, a positive link is established between a country’s development, characterized by high GDPs and low inflation and interest rates, and the precision of Islamic banks’ survey responses. Analyzing risk-related concerns, the study notes a significant reduction in credit portfolio risk attributed to improved risk management practices, global economic growth, stricter regulations, and diversified asset portfolios. Concerns related to terrorism financing and cybersecurity risks have also decreased due to the better enforcement of anti-money laundering regulations and investments in cybersecurity infrastructure and education. This research enhances our understanding of risk management in Islamic banks, highlighting the impact of bank size and country development. Additionally, it emphasizes the need for ongoing analysis beyond 2021 to account for potential COVID-19 effects and evolving risk management and regulatory practices in Islamic banking.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Risk and Financial Management<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/jrfm17030104" target="_blank">https://dx.doi.org/10.3390/jrfm17030104</a></p>2024-03-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/jrfm17030104https://figshare.com/articles/journal_contribution/Predictive_Power_of_Random_Forests_in_Analyzing_Risk_Management_in_Islamic_Banking/29117123CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291171232024-03-01T00:00:00Z |
| spellingShingle | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking Ahmet Faruk Aysan (11902115) Commerce, management, tourism and services Banking, finance and investment Business systems in context Strategy, management and organisational behaviour Information and computing sciences Cybersecurity and privacy Philosophy and religious studies Religious studies Risk management Islamic banks Survey analysis Random forest Machine learning |
| status_str | publishedVersion |
| title | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| title_full | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| title_fullStr | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| title_full_unstemmed | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| title_short | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| title_sort | Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking |
| topic | Commerce, management, tourism and services Banking, finance and investment Business systems in context Strategy, management and organisational behaviour Information and computing sciences Cybersecurity and privacy Philosophy and religious studies Religious studies Risk management Islamic banks Survey analysis Random forest Machine learning |