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Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia 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/approachThis 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.FindingsThe 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/implicationsThis 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 implicationsOwing 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/valueAccording 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

Ledhem, Mohammed Ayoub
Journal of Modelling in Management , Volume 17 (3): 20 – Aug 22, 2022
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References (41)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
eISSN
1746-5664
DOI
10.1108/jm2-10-2020-0286
Publisher site
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Abstract

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/approachThis 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.FindingsThe 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/implicationsThis 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 implicationsOwing 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/valueAccording 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.

Journal

Journal of Modelling in Management – Emerald Publishing

Published: Aug 22, 2022

Keywords: Data mining; Performance management; Banking; Financial analysis; Data analysis

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