Data mining techniques for predicting the financial performance of Islamic banking in Indonesia
Journal of Modelling in Management
ISSN: 1746-5664
Article publication date: 1 July 2021
Issue publication date: 22 August 2022
Abstract
Purpose
The purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training.
Design/methodology/approach
This paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance.
Findings
The experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking.
Research limitations/implications
This paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques.
Practical implications
Owing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector.
Originality/value
According to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.
Keywords
Acknowledgements
The author would like to thank the General Directorate of Scientific Research and Technological Development “La Direction Générale de la Recherche Scientifique et du Développement Technologique (DGRSDT)” under the the Algerian Ministry of Higher Education and Scientific Research “le Ministère de l’Enseignement Supérieur et la Recherche Scientifique” for sponsoring this research. The author would also like to thank the Laboratory for Money and Financial Institutions in the Arab Maghreb “MIFMA laboratory” (Algeria) for the continuous support of the research.
The author would also like to give special thanks to the head of the LEPPESE Laboratory at the University Centre of Maghnia (Algeria), Prof. Dr. Mekidiche Mohammed (Jazak-Allahu-Khayran Estadi-Elkarim) for the continuous encouragement to implement the scientific research if and only if with high quality and significant value.
Citation
Ledhem, M.A. (2022), "Data mining techniques for predicting the financial performance of Islamic banking in Indonesia", Journal of Modelling in Management, Vol. 17 No. 3, pp. 896-915. https://doi.org/10.1108/JM2-10-2020-0286
Publisher
:Emerald Publishing Limited
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