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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Ayoub Ledhem
Keyword(s):  
Data Mining ◽  
Financial Performance ◽  
Banking Sector ◽  
Capital Adequacy ◽  
Future Research ◽  
Islamic Banking ◽  
Accuracy Score ◽  
Content Type ◽  
Data Mining Techniques ◽  
Using Data

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.

A consensus-based corporate governance paradigm for Islamic banks

2014 ◽  
Vol 6 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Monal Abdel-Baki ◽  
Valerio Leone Sciabolazza
Keyword(s):  
Corporate Governance ◽  
Financial Performance ◽  
Banking Sector ◽  
Performance Metrics ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Content Type ◽  
Sustainable Finance ◽  
Market Driven ◽  
The Needy

Purpose – Islamic banking is a viable sustainable banking model that has shown resilience to financial crises. The aim of this research is to design a consensus-based ethical and market-driven corporate governance index (CGI) to boost financial performance and ensure compliance with Islamic rulings. Design/methodology/approach – The design of the CGI is the outcome of the feedback obtained from a cross-country survey to measure bank efforts in enhancing corporate governance (CG) throughout the ten-year period of 2001-2011. The CGI is divided into six core CG themes and 40 sub-themes. Findings – First, the results of the multiple regression analysis show a consistent positive relationship between CG and financial performance metrics. Second, the authors detect misaligned compensation structures for directors. Third, poor governance leads to higher risk exposures. Research limitations/implications – CG in Islamic banks is yet an evolving discipline and infant practice. This research aims to introduce a CGI that should be updated and improved as the discipline evolves. Practical implications – The research concludes by proposing a CG paradigm. The outcome of the research could also be of use to both Islamic banks and to the rapidly growing sustainable banking sector in designing a similar CGI and CG model incorporating the ethical features of sustainable finance. Social implications – The core ethos of Islam are: avoiding the exploitation of the needy, avoiding excessively risky transactions, avoiding unethical transactions and justice, equity and income redistribution. If properly applied, Islamic banking will display all features of sustainable finance as well as enhance social welfare. Originality/value – To the best of the authors' knowledge, this is the first CGI that is based on an ethical and all-inclusive input of all stakeholders.

Toward a Framework for Advanced Query Processing

2001 ◽  
pp. 317-330
Author(s):  
Suk-Chung Yoon
Keyword(s):  
Data Mining ◽  
Computational Complexity ◽  
Query Processing ◽  
Final Section ◽  
Future Research ◽  
Generation Process ◽  
Data Mining Techniques ◽  
Query Answer ◽  
Different Types ◽  
Using Data

The contribution of our approach is that we develop a framework for processing and answering queries flexibly by applying data mining techniques. In addition, we suggest strategies to reduce the computational complexity of the advanced query answer generation process. We believe that our approach enhances user-machine interfaces significantly to conventional databases with additional features. This chapter is structured as follows. The next section introduces motivating examples to show the advantages of advanced query processing. Following that we survey related works on intelligent query processing. Then we present our approach to process different types of queries using data mining techniques. The final section discusses our conclusions and possible extensions of our work for future research.

Ethical consideration of Islamic banks in Pakistan: an empirical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima Khaleel ◽  
Pervez Zamurrad Janjua ◽  
Mumtaz Ahmed
Keyword(s):  
Banking Sector ◽  
Annual Reports ◽  
Survey Method ◽  
Future Research ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Ethical Values ◽  
Content Type ◽  
Islamic Bank ◽  
Finance Ethics

Purpose The purpose of this paper is threefold. First, it assesses communicated (information disclosed in annual reports and websites) ethical values of Islamic banks (IBs) by using an index based on Islamic precept. Second, this research paper analyzes the perception of employees working in IBs of Pakistan regarding previously mentioned dimensions constructed in the form of index. Third, it explores the difference (if any) between communicated and perceived ethical values of IBs in Pakistan. Design/methodology/approach This study incorporated two research methods, namely, content analysis (qualitative method) and descriptive analysis (quantitative method) to assess communicated and perceived ethical values. A checklist was designed that includes total six dimensions with 106 items or constructs. For perceived ethics, survey method is used to explore how far in practice IBs are operating in line with Islamic finance ethics in Pakistan by distributing questionnaires among employees. Findings This research study revealed overall satisfactory communicated and perceived ethical values in IBs of Pakistan. It is generally concluded that Meezan Bank is doing well in all dimensions as compare to other three banks in Pakistan. Some banks such as Dubai Islamic Bank and Albaraka Islamic bank lack proper format of annual reports. It recommended proper training and development of employees particularly about Islamic banking products and procedure. Moreover, it is recommended to take initiative of attracting female segment of the society and environment protection related campaigns. Research limitations/implications Because of data and time constraints, an extended beneficiary analysis could not be materialized in this study. Therefore, for future research, it is recommended to expand the stakeholders’ analysis beyond employees of IBs. Practical implications This study may be helpful for policymakers and other stakeholders to improve the image and for further growth of IBs in Pakistan. Social implications This study is the part of corporate social responsibility, so it will add value to social norms of banking sector and provide different dimensions and constructs based on Islamic ethical and moral system. It highlights banker’s responsibilities toward society. Originality/value This paper supports the phenomena of Islamic banking and finance in emerging markets and shows its potential growth for the economy.

Who creates value in a user innovation community? A case study of MyStarbucksIdea.com

2016 ◽  
Vol 40 (2) ◽  
pp. 170-186 ◽  
Author(s):  
Hanjun Lee ◽  
Yongmoo Suh
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Open Innovation ◽  
Information Overload ◽  
Classification Models ◽  
Content Type ◽  
Data Mining Techniques ◽  
Using Data ◽  
Basic Features ◽  
Innovation Community

Purpose – Successful open innovation requires that many ideas be posted by a number of users and that the posted ideas be evaluated to find ideas of high quality. As such, successful open innovation community would have inherently information overload problem. The purpose of this paper is to mitigate the information problem by identifying potential idea launchers, so that they can pay attention to their ideas. Design/methodology/approach – This research chose MyStarbucksIdea.com as a target innovation community where users freely share their ideas and comments. We extracted basic features from idea, comment and user information and added further features obtained from sentiment analysis on ideas and comments. Those features are used to develop classification models to identify potential idea launchers, using data mining techniques such as artificial neural network, decision tree and Bayesian network. Findings – The results show that the number of ideas posted and the number of comments posted are the most significant among the features. And most of comment-related sentiment features found to be meaningful, while most of idea-related sentiment features are not in the prediction of idea launchers. In addition, this study show classification rules for the identification of potential idea launchers. Originality/value – This study dealt with information overload problem in an open innovation context. A large volume of textual customer contents from an innovation community were examined and classification models to mitigate the problem were proposed using sentiment analysis and data mining techniques. Experimental results show that the proposed classification models can help the firm identify potential idea launchers for its efficient business innovation.

scholarly journals Towards reliable prediction of academic performance of architecture students using data mining techniques

2018 ◽  
Vol 16 (3) ◽  
pp. 385-397 ◽  
Author(s):  
Ralph Olusola Aluko ◽  
Emmanuel Itodo Daniel ◽  
Olalekan Shamsideen Oshodi ◽  
Clinton Ohis Aigbavboa ◽  
Abiodun Olatunji Abisuga
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Logistic Regression ◽  
Academic Performance ◽  
Predictive Accuracy ◽  
Support Vector ◽  
Content Type ◽  
Data Mining Techniques ◽  
Svm Model ◽  
Using Data

Purpose In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement. Design/methodology/approach The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. Findings The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students. Research limitations/implications Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. Originality/value The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasim Ansari ◽  
Hossein Vakilimofrad ◽  
Muharram Mansoorizadeh ◽  
Mohamad Reza Amiri
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Recommender Systems ◽  
High Efficiency ◽  
Good Opportunity ◽  
Content Type ◽  
Data Mining Techniques ◽  
The Real ◽  
Using Data ◽  
Delay Prediction

Purpose This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques. Design/methodology/approach The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed. Findings The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty. Practical implications The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users. Originality/value The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

scholarly journals An assessment of the financial soundness of the Kazakh banks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aigul P. Salina ◽  
Xin Zhang ◽  
Omaima A.G. Hassan
Keyword(s):  
Banking Sector ◽  
Banking System ◽  
Principal Component ◽  
Capital Adequacy ◽  
Content Type ◽  
Bank Monitoring ◽  
The World ◽  
Financial Soundness ◽  
Using Data ◽  
Asset Quality

PurposeThe contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of nonperforming loans (NPL) to total gross loans in 2012.Design/methodology/approachUsing data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, the study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). Then, it uses the outcomes of the PCA in a cluster analysis and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and proved to be more reliable in detecting risky banks.FindingsFifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy, return on assets, profitability, asset quality, liquidity and leverage. Then using the “k-means” method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus, in 2014 a new group of banks has emerged, i.e. financially unsound banks.Practical implicationsThe proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes us advocate its employability for bank monitoring and supervision purposes.Originality/valueThis study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.

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