Indonesian Islamic moral incentives in credit card debt repayment: a feature selection using various data mining

Rezzy Eko Caraka, Fahmi Ali Hudaefi, Prana Ugiana, Toni Toharudin, Avia Enggar Tyasti, Noor Ell Goldameir and Rung Ching Chen

International Journal of Islamic and Middle Eastern Finance and Management, 2021, vol. 15, issue 1, 100-124

Abstract: Purpose - Despite the practice of credit card services by Islamic financial institutions (IFIs) is debatable, Islamic banks (IBs) have been offering this product. Both Muslim and non-Muslim customers have subscribed to the products. Thus, it is critical to analyse the strategy of IBs’ moral messages in reminding their Muslim and non-Muslim customers to repay their credit card debts. This paper aims to investigate this issue in Indonesia using data mining via machine learning. Design/methodology/approach - This study examines the IBs’ customers across the 32 provinces of Indonesia regarding their moral status in credit card debt repayment. This work considers 6,979 observations of the variables that affect the moral status of the IBs’ customers in repaying their debt. The five types of data mining via machine learning (i.e. Boruta, logistic regression, Bayesian regression, random forest, XGBoost and spatial cluster) are used. Boruta, random forest and XGBoost are used to select the important features to investigate the moral aspects. Bayesian regression is used to get the odds and opportunity for the transition of each variable and spatially formed based on the information from the logistical intercepts. The best method is selected based on the highest accuracy value to deliver the information on the relationship between moral status categories in the selected 32 provinces in Indonesia. Findings - A different variable on moral status in each province is found. The XGBoost finds an accuracy value of 93.42%, which the three provincial groups have the same information based on the importance of the variables. The strategy of IBs’ moral messages by sending the verse of al-Qur’an and al-Hadith (traditions or sayings of the Prophet Muhammad PBUH) and simple messages reminders do not impact the customers’ repaying their debts. Both Muslim and non-Muslim groups are primarily found in the non-moral group. Research limitations/implications - This study does not consider socio-economic demographics and culture. This limitation calls future works to consider such factors when conducting a similar topic. Practical implications - The industry professionals can take benefit from this study to understand the Indonesian customers’ moral status in repaying credit card debt. In addition, future works may advance the recent findings by considering socio-cultural factors to investigate the moral status approach to Islamic credit warnings that is not covered by this study. Social implications - This work finds that religious text of credit card repayment reminders sent to Muslims in several provinces of Indonesia does not affect their decision to repay their debts. To some extent, this finding draws a social issue that the local IBs need to consider when implementing the strategy of credit card repayment reminders. Originality/value - This study credits a novelty in the discourse of data science for Islamic finance practices. Specifically, this study pioneers an example of using data mining to investigate Islamic-moral incentives in credit card debt repayment.

Keywords: Moral status; Credit card; Data mining; Repayment; Machine learning; Feature selection; Islamic credit card (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:imefmp:imefm-08-2020-0408

DOI: 10.1108/IMEFM-08-2020-0408

Access Statistics for this article

International Journal of Islamic and Middle Eastern Finance and Management is currently edited by Prof M. Kabir Hassan

More articles in International Journal of Islamic and Middle Eastern Finance and Management from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().