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    Systemic Risk Measures and Machine Learning Algorithms in Islamic and Conventional Financial Institutions

    Access Status
    Fulltext not available
    Embargo Lift Date
    2026-07-10
    Authors
    Sajjad, Shakeel
    Date
    2024
    Supervisor
    Dhanuskodi Rengasamy
    Peter Cincinelli
    Rocky J. Dwyer
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/95592
    Collection
    • Curtin Theses
    Abstract

    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.

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