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ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS

Author

Listed:
  • Saiful Anwar

    (PT. Bank BRI Syariah)

  • A.M Hasan Ali

    (UIN Syarif Hidayatullah)

Abstract

This research proposes a development of Early Warning System (EWS) model towards the financial performance of Islamic bank using financial ratios and macroeconomic indicators. The result of this paper is ready-to-use algorithm for the issue that needs to be solved shortly using machine learning technique which is not widely applied in Islamic banking. The research was conducted in three stages using Artificial Neural Networks (ANNs) technique: the selection of variables that significantly affect financial performance, developing an algorithm as a predictor and testing the predictor algorithm using out of sample data. Finally, the research concludes that the proposed model results in 100% accuracy for predicting Islamic bank’s financial conditions for the next two consecutive months.

Suggested Citation

  • Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
    • Handle: RePEc:idn:journl:v:20:y:2018:i:3d:p:325-342
    DOI: https://doi.org/10.21098/bemp.v20i3.856
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    More about this item

    Keywords

    Early Warning System; Artificial Neural Networks; Islamic Banks; Financial Distress;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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