Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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
Download PDF
20 pages

Loading next page...
 
/lp/emerald-publishing/data-mining-techniques-for-predicting-the-financial-performance-of-0qVd0pqmcI
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
eISSN
1746-5664
DOI
10.1108/jm2-10-2020-0286
Publisher site
See Article on Publisher Site

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

References

  • A neural networks system for predicting Islamic banks performance
    Al-Osaimy, M.H.
  • Data envelopment analysis and data mining to efficiency estimation and evaluation
    Anouze, A.L.M.; Bou-Hamad, I.
  • ANNs-based early warning system for Indonesian Islamic banks
    Anwar, S.; Ali, A.M.H.
  • Robustness analysis of artificial neural networks and support vector machine in making prediction
    Anwar, S.; Ismal, R.
  • Comparing accuracy performance of ANN, MLR, and GARCH model in predicting time deposit return of Islamic bank
    Anwar, S.; Mikami, Y.
  • Predicting future depositors rate of return applying neural network: a case-study of Indonesian Islamic bank
    Anwar, S.; Watanabe, K.
  • Behavior investigation of Islamic bank deposit return utilizing artificial neural networks model
    Anwar, S.; Ismal, R.; Watanabe, K.
  • Applying multiple linear regression and neural network to predict bank performance
    Bakar, N.M.A.; Tahir, I.M.
  • Bagging predictors
    Breiman, L.
  • A very fast learning method for neural networks based on sensitivity analysis
    Castillo, E.; Guijarro-Berdiñas, B.; Fontenla-Romero, O.; Alonso-Betanzos, A.
  • State of the global Islamic economy report 2019/20
  • Comparison and predicting financial performance of Islamic and conventional banks in Indonesia to achieve growth sustainability
    Fakhri, U.N.; Anwar, S.; Ismal, R.; Ascarya, A.
  • Thomson reuters
  • Modern data mining tools in descriptive sensory analysis: a case study with a random Forest approach
    Granitto, P.M.; Gasperi, F.; Biasioli, F.; Trainotti, E.; Furlanello, C.
  • Estimation of cauliflower weight based on multiple linear regression modelling
    Guo, X.-X.; Zhou, G.-H.; Cheng, H.
  • Data mining for banking and finance
    Hammawa, M.B.
  • The elements of statistical learning: data mining, inference and prediction
    Hastie, T.; Tibshirani, R.; Friedman, J.; Franklin, J.
  • REDmoney
  • Islamic financial services board (IFSB)
  • DinarStandard
  • A review on data mining in banking sector
    Jayasree, V.; Balan, R.V.S.
  • Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: bank branches
    Kassani, S.H.; Kassani, P.H.; Najafi, S.E.
  • Economic growth and financial performance of Islamic banks: a CAMELS approach
    Ledhem, M.A.; Mekidiche, M.
  • Applying enhanced data mining approaches in predicting bank performance: a case of Taiwanese commercial banks
    Lin, S.-W.; Shiue, Y.-R.; Chen, S.-C.; Cheng, H.-M.
  • Assessing credit risk: an application of data mining in a rural bank
    Mandala, I.G.N.N.; Nawangpalupi, C.B.; Praktikto, F.R.
  • Comparing ridge and LASSO estimators for data analysis
    Melkumova, L.E.; Shatskikh, S.Y.
  • Use of data mining in banking
    Moin, K.I.; Ahmed, D.Q.B.
  • A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks
    Moradi, S.; Mokhatab Rafiei, F.
  • Financial distress prediction of Iranian companies using data mining techniques
    Moradi, M.; Salehi, M.; Ghorgani, M.E.; Yazdi, H.S.
  • Role of data mining in retail sector
    Ramageri, B.M.; Desai, B.L.
  • Islamic finance development report 2018: building momentum
    Reuters, T.
  • Camels and performance evaluation of banks in Malaysia: conventional versus Islamic
    Rozzani, N.; Rahman, R.A.
  • Learning representations by back-propagating errors
    Rumelhart, D.E.; Hinton, G.E.; Williams, R.J.
  • Determinants of financial performance of Islamic banking in Malaysia
    Samail, N.A.B.; Zaidi, N.S.B.; Mohamed, A.S. B.; bin Kamaruzaman, M.N.
  • Predicting the performance and survival of Islamic banks in Malaysia to achieve growth sustainability
    Sapuan, N.M.; Bakar, S.; Ramlan, H.
  • Robust front-end for audio, visual and audio–visual speech classification
    Terissi, L.D.; Sad, G.D.; Gómez, J.C.
  • Data mining: prediction methods
    Urso, A.; Fiannaca, A.; La Rosa, M.; Ravì, V.; Rizzo, R.
  • Predicting efficiency in Malaysian Islamic banks: a two-stage TOPSIS and neural networks approach
    Wanke, P.; Azad, M.D.A.K.; Barros, C.P.