OPTIMIZING SHARIA PRINCIPLES THROUGH ARTIFICIAL INTELLIGENCE: A JURIDICAL-ECONOMIC INQUIRY INTO COMBATING FRAUD IN ISLAMIC FINANCIAL INSTITUTIONS

  • Early Ridho Kismawadi IAIN LANGSA
  • T. Hervasha IAIN LANGSA
  • Muhammad Syahril IAIN Langsa
Keywords: Artificial Intelligence, Islamic Finance, Blockchain, Algorithms, Fraud Detection

Abstract

The main objective of this research is to examine how artificial intelligence can be used to optimise the application of sharia principles in Islamic financial institutions. The research will specifically focus on conducting juridical-economic investigations to prevent fraud. This research seeks to enhance the security of transactions and operations in Islamic financial institutions by assessing the role of artificial intelligence. It also aims to examine the ethical efficiency and compliance implications of integrating artificial intelligence with sharia principles. Additionally, the research aims to create predictive models and algorithms using artificial intelligence to identify fraud patterns that contravene sharia principles. Furthermore, it aims to establish a legal framework that facilitates the use of artificial intelligence in combating fraud within institutions. The findings demonstrate that the implementation of artificial intelligence can greatly enhance the security of transactions and operations within Islamic financial institutions. The incorporation of artificial intelligence into sharia principles enhances efficiency and ethical adherence. Additionally, the creation of prediction models and artificial intelligence algorithms effectively identifies fraudulent tendencies that contravene sharia rules. The research has several consequences, namely, the need to guarantee the security of Islamic financial services, enhance public confidence, and promote the establishment of policies that foster innovation in the Islamic financial sector. The report suggests that Islamic financial institutions should adopt artificial intelligence as a proactive measure to combat fraud. Governments might utilise the research findings as a basis for formulating policies that facilitate the incorporation of artificial intelligence in Islamic financial institutions. These proposals urge technology suppliers, Islamic consultants, and regulators to actively participate in applying research findings, guiding ethical norms, and establishing rules that promote innovation in the Islamic financial industry. Therefore, this research significantly contributes to the development of a financial ecosystem that prioritises safety, fairness, and adherence to sharia principles for the betterment of society.

References

Abbas, K., & Hafeez, M. (2021). Wıll Artıfıcıal Intellıgence Rejuvenate Islamıc Fınance? A Versıon of World Academıa. Hitit Theology Journal, 20(3), 311–324. https://doi.org/10.14395/hid.931401

Abdeen, M., Jan, S., Khan, S., & Ali, T. (2019). Employing Takaful islamic banking through state of the art blockchain: A case study. International Journal of Advanced Computer Science and Applications, 10(12), 648–654. https://doi.org/10.14569/ijacsa.2019.0101283

Agrawal, R., Majumdar, A., Kumar, A., & Luthra, S. (2023). Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities. Operations Management Research, 16(4), 1720–1741. https://doi.org/10.1007/s12063-023-00383-y

Al Shehab, N., & Hamdan, A. (2021). Artificial intelligence and women empowerment in bahrain. In Studies in Computational Intelligence (Vol. 954, pp. 101–121). https://doi.org/10.1007/978-3-030-72080-3_6

Ambeth Kumar, V. D., Varadarajan, V., Gupta, M. K., Rodrigues, J. J. P. C., & Janu, N. (2022). AI Empowered Big Data Analytics for Industrial Applications. Journal of Universal Computer Science, 28(9), 877–881. https://doi.org/10.3897/jucs.94155

Andrade, I. M. D., & Tumelero, C. (2022). Increasing customer service efficiency through artificial intelligence chatbot. Revista de Gestao, 29(3), 238–251. https://doi.org/10.1108/REGE-07-2021-0120

Buyuktepe, O., Catal, C., Kar, G., Bouzembrak, Y., Marvin, H., & Gavai, A. (2023). Food fraud detection using explainable artificial intelligence. Expert Systems. https://doi.org/10.1111/exsy.13387

Demertzis, K., Rantos, K., Magafas, L., Skianis, C., & Iliadis, L. (2023). A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System. Information (Switzerland), 14(9). https://doi.org/10.3390/info14090477

Dong, S., Huang, Y., Jin, X., Yang, G., Nie, A., Qi, X., Cheng, Y., & Wu, H. (2023). Development status and trend of high-density 3D seismic exploration technology for coal fields. Meitiandizhi Yu Kantan/Coal Geology and Exploration, 51(2), 273–282. https://doi.org/10.12363/issn.1001-1986.23.03.0116

Giovanola, B., & Tiribelli, S. (2023). Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms. AI and Society, 38(2), 549–563. https://doi.org/10.1007/s00146-022-01455-6

Gonçalves, A. R., Pinto, D. C., Rita, P., & Pires, T. (2023). Artificial Intelligence and Its Ethical Implications for Marketing. Emerging Science Journal, 7(2), 313–327. https://doi.org/10.28991/ESJ-2023-07-02-01

Guggenberger, T., Kühne, D., Schlatt, V., & Urbach, N. (2023). Designing a cross-organizational identity management system: Utilizing SSI for the certification of retailer attributes. Electronic Markets, 33(1). https://doi.org/10.1007/s12525-023-00620-z

Hemdan, E. E.-D., El-Shafai, W., & Sayed, A. (2023). Integrating Digital Twins with IoT-Based Blockchain: Concept, Architecture, Challenges, and Future Scope. Wireless Personal Communications, 131(3), 2193–2216. https://doi.org/10.1007/s11277-023-10538-6

Hsu, C.-L., & Lin, J. C.-C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services, 71. https://doi.org/10.1016/j.jretconser.2022.103211

Kim, S.-K. (2022). Blockchain Smart Contract to Prevent Forgery of Degree Certificates: Artificial Intelligence Consensus Algorithm. Electronics (Switzerland), 11(14). https://doi.org/10.3390/electronics11142112

Moore, E., Imteaj, A., Rezapour, S., & Amini, M. H. (2023). A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-Constrained Computing. IEEE Internet of Things Journal, 10(24), 21942–21958. https://doi.org/10.1109/JIOT.2023.3313055

Mostafa, A. M., Rushdy, E., Medhat, R., & Hanafy, A. (2023). An identity management scheme for cloud computing: Review, challenges, and future directions. Journal of Intelligent and Fuzzy Systems, 45(6), 11295–11317. https://doi.org/10.3233/JIFS-231911

Obayya, M., Nemri, N., Nour, M. K., Al Duhayyim, M., Mohsen, H., Rizwanullah, M., Sarwar Zamani, A., & Motwakel, A. (2022). Explainable Artificial Intelligence Enabled TeleOphthalmology for Diabetic Retinopathy Grading and Classification. Applied Sciences (Switzerland), 12(17). https://doi.org/10.3390/app12178749

Oktariatas Kesumayuda, A. (2019). International organization of securities commissions role on transactions in Indonesia Sharia Capital Market. International Journal of Innovation, Creativity and Change, 6(8), 156–164. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073143651&partnerID=40&md5=f4cdc1c0ff6a58830400fbbb23251f5a

Paraman, P., & Anamalah, S. (2023). Ethical artificial intelligence framework for a good AI society: principles, opportunities and perils. AI and Society, 38(2), 595–611. https://doi.org/10.1007/s00146-022-01458-3

Perko, I. (2023). Data sharing concepts: a viable system model diagnosis. Kybernetes, 52(9), 2976–2991. https://doi.org/10.1108/K-04-2022-0575

Prasad, S. N., & Rekha, C. (2023). Block chain based IAS protocol to enhance security and privacy in cloud computing. Measurement: Sensors, 28. https://doi.org/10.1016/j.measen.2023.100813

Rjoub, H., Adebayo, T. S., & Kirikkaleli, D. (2023). Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-023-00469-3

Rodrigues, V. F., Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., da Costa, C. A., Barbosa, J. L. V, Antunes, R. S., Scorsatto, R., & Arcot, T. (2022). Fraud detection and prevention in e-commerce: A systematic literature review. Electronic Commerce Research and Applications, 56. https://doi.org/10.1016/j.elerap.2022.101207

Singh, P. D., Kaur, R., Dhiman, G., & Bojja, G. R. (2023). BOSS: A new QoS aware blockchain assisted framework for secure and smart healthcare as a service. Expert Systems, 40(4). https://doi.org/10.1111/exsy.12838

Sood, P., Sharma, C., Nijjer, S., & Sakhuja, S. (2023). Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing. International Journal of System Assurance Engineering and Management, 14(6), 2120–2135. https://doi.org/10.1007/s13198-023-02043-7

Vetrivendan, L., & Kumar, G. (2023). CCNN: An Artificial Intelligent based Classifier to Credit Card Fraud Detection System with Optimized Cognitive Learning Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11, 159–171. https://doi.org/10.17762/ijritcc.v11i5s.6640

Wang, Y.-C., Chen, T.-C. T., & Chiu, M.-C. (2023). An improved explainable artificial intelligence tool in healthcare for hospital recommendation. Healthcare Analytics, 3. https://doi.org/10.1016/j.health.2023.100147

Ye, Z., Chen, C.-L., Weng, W., Sun, H., Tsaur, W.-J., & Deng, Y.-Y. (2023). An anonymous and fair auction system based on blockchain. Journal of Supercomputing, 79(13), 13909–13951. https://doi.org/10.1007/s11227-023-05155-w

Yuspin, W., Wardiono, K., Budiono, A., & Gulyamov, S. (2022). The Law Alteration on Artificial Intelligence in Reducing Islamic Bank’s Profit and Loss Sharing Risk. Legality: Jurnal Ilmiah Hukum, 30(2), 267–282. https://doi.org/10.22219/ljih.v30i2.23051

Published
2024-02-23