Risk Management Strategy In Islamic Banks: An Artificial Intelligence Approach
DOI:
https://doi.org/10.55657/jpmb.v4i01.227Keywords:
Risk management, islamic banks, artificial intelligence, sharia complianceAbstract
The development of digital technology has brought significant changes to the banking industry, including Islamic banking. One crucial aspect that has gained attention is risk management, considering that Islamic banks operate based on Sharia principles, which avoid riba (interest), gharar (uncertainty), and maysir (gambling). This study discusses risk management strategies in Islamic banks using an Artificial Intelligence (AI) approach as an innovative solution to enhance the efficiency and effectiveness of risk mitigation. AI offers several advantages, such as predictive analytics, anomaly detection, and automation in managing credit, liquidity, operational, and Sharia compliance risks. By leveraging AI technology, Islamic banks can identify risk patterns more quickly, optimize profit-sharing-based financing decisions, and improve compliance with applicable regulations. This research employs a qualitative descriptive approach through literature review and secondary data analysis to illustrate how AI can be applied in Islamic banking risk management. The findings indicate that integrating AI into the Islamic banking system not only strengthens resilience to risks but also supports transparency, operational efficiency, and customer trust. However, this study is limited by its reliance on secondary data and literature sources, which may not fully capture real-time implementation challenges faced by Islamic banks. Therefore, AI-based risk management strategies represent a strategic step to enhance the competitiveness of Islamic banks in the digital era.
References
Ahmed, Amr Mohamed El Tiby Grais, W. (2014). Islamic finance and economic development: Risk, regulation, and corporate governance. John Wiley & Sons.
Ahmed, M., & Shakil, M. (2020). Artificial Intelligence in Banking and Finance: The Revolution in Risk Management. Wiley.
Ali, M. & Ahmad, M. (2020). Artificial intelligence in Islamic banking: Challenges and opportunities. Springer.
Ali, M. (2019). Islamic Finance: Law, Economics, and Practice. Cambridge University Press.
Alqahtani, A. (2021). Artificial intelligence in risk management of Islamic banks: Opportunities and challenges. International Journal of Islamic Financial Studies.
Alqahtani, F., & Mayes, D. G. (2018). Financial stability of Islamic banking and the global financial crisis: Evidence from the Gulf Cooperation Council. Economic Systems, 42(2), 346–360.
Amin, M. (2020). Challenges in implementing artificial intelligence in Islamic banking: A case study. International Journal of Islamic Banking, 12(3), 45–60.
Antonio, M. S. (2001). Bank syariah: dari teori ke praktik. Gema Insani.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, Vol. 13, No. 3, pp. 319–340. DOI: 10.2307/249008.
Diawati, Diana, N., & Mawardi, M. C. (2022). Analisis Risiko Dan Strategi Mitigasi Risiko Pada Layanan Fintech Syariah Peer To Peer Lending Di Masa Pandemi Covid-19 (Studi Kasus: PT. Alami Fintek Sharia). El-Aswaq, 19(2), 1–13. http://riset.unisma.ac.id/index.php/laswq/article/ download/16215/12248
Gustanto, E. S., Wicaksono, M. A., Arfaizar, J. (2024). Perbankan Syariah 5.0. Penerbit Samudra Biru.
Hassan, A., & Al-Debei, M. (2020). Machine learning applications in Islamic banking: A systematic review. Journal of Islamic Financial Studies.
Ismail, A. (2017a). Risk Management in Islamic Financial Institutions: Framework and Practices. Springer.
Ismail, A. (2017b). Shariah compliance in Islamic finance and AI applications. Journal of Islamic Finance, 9(1), 24–35.
Kumar, S., & Sharma, M. (2018). Artificial intelligence in finance: Applications and challenges. Springer.
Latifah, H. R. (2023). Analisis Kualitas Layanan Berbasis Artificial Intelligence pada Nasabah Bank Syariah Indonesia. Universitas Islam Indonesia.
Miller, C. & Smith, B. (2022). Big data analytics and machine learning in financial risk management. Elsevier.
Nugroho, M.A., Manzilati, A., Ekawaty, M., Badriyah, N. and Sulaeman, S. (2025), "Factors driving the growth of the halal industry in top GIEI countries: the role of domestic investment, FDI and economic freedom", Journal of Islamic Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JIMA-07-2024-0273
Nurkhasanah, L., Gunardi, S., Gustanto, E. S., & Kurniawan, M. A. (2023). The Influence of Third-Party Funds, Non-Performing Finance, and Return on Assets on Murabahah Financing in Islamic Commercial Banks During the COVID-19 Pandemic. Mutanaqishah: Journal of Islamic Banking, 3(1), 1–15.
Putro, R. L., Rapini, T., & Farida, U. (2025). Analisis Penerapan Kecerdasan Buatan (Artificial Intelligence) untuk Meningkatkan Keamanan Finansial Nasabah pada Sektor Perbankan Universitas Muhammadiyah Ponorogo , Indonesia. Lokawati : Jurnal Penelitian Manajemen Dan Inovasi Riset, 3(1), 79–91.
Rahim, S. (2014). Managing risk in Islamic banks. Cambridge University Press.
Ramadhan, M. R., Sagita, N., & Hendra, J. (2024). Perbandingan Risiko Pembiayaan Pada Bank Syariah Dan Bank Konvensional Di Indonesia. Jurnal Masharif Al-Syariah: Jurnal Ekonomi Dan Perbankan Syariah, 9(5).
Sunaryo, D., Hamdan, A. A., & Cecilia Winata, D. D. A. (2024). Prediksi tren risiko keuangan perusahaan berdasarkan model machine learning (ARIMA): Tinjauan literatur. Jurnal Akuntansi Manajemen, 3(2), 78–94.
Sweeney, L. (2020). Data privacy and security challenges in the use of AI in financial systems. Journal of Data Protection & Privacy.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Edo Segara Gustanto, Anton Priyo Nugroho, Muhammad Arif Yahya
This work is licensed under a Creative Commons Attribution 4.0 International License.
