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Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking

Author

Listed:
  • Ahmet Faruk Aysan

    (Non-Resident Fellow Middle East Council for Global Affairs (MECGA), Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar)

  • Bekir Sait Ciftler

    (Data Science & Artificial Intelligence Department, College of Computing and IT, University of Doha for Science and Technology, Doha P.O. Box 34110, Qatar)

  • Ibrahim Musa Unal

    (College of Islamic Studies, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar)

Abstract

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.

Suggested Citation

  • Ahmet Faruk Aysan & Bekir Sait Ciftler & Ibrahim Musa Unal, 2024. "Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking," JRFM, MDPI, vol. 17(3), pages 1-19, March.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:3:p:104-:d:1349606
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    References listed on IDEAS

    as
    1. Ahmet Faruk Aysan & Ibrahim Musa Unal, 2021. "Is Islamic Finance Evolving Into Fintech and Blockchain: A Bibliometric Analysis," Post-Print hal-03351153, HAL.
    2. Aebi, Vincent & Sabato, Gabriele & Schmid, Markus, 2012. "Risk management, corporate governance, and bank performance in the financial crisis," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3213-3226.
    3. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
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