AI-Driven Fraud Detection and Financial Security Framework for Saudi Banking Systems

References

[1] Al-Dosari, K., Fetais, N., & Kucukvar, M. (2022). Artificial intelligence and cyber defense system for banking industry: A qualitative study of AI applications and challenges. Cybernetics and Systems, 55(2), 302-330.

[2] Almutairi, M., & Nobanee, H. (2020). Artificial intelligence in financial industry. SSRN Electronic Journal.

[3] Bouteraa, M., Raja Hisham, R. R. I., & Zainol, Z. (2022). Challenges affecting bank consumers' intention to adopt green banking technology in the UAE: A UTAUT-based mixed-methods approach. Journal of Islamic Marketing, 14(10), 2466-2501.

[4] Caldwell, M., Andrews, J. T. A., Tanay, T., & Griffin, L. D. (2020). AI-enabled future crime. Crime Science, 9(1), 1-13.

[5] Chaudhry, U. B., & Hydros, A. K. M. (2023). Zero-trust-based security model against data breaches in the banking sector: A blockchain consensus algorithm. IET Blockchain, 3(2), 98-115.

[6] Chen, J., Henry, E., & Jiang, X. (2022). Is cybersecurity risk factor disclosure informative? Evidence from disclosures following a data breach. Journal of Business Ethics, 187(1), 199-224.

[7] Choithani, T., Chowdhury, A., Patel, S., Patel, P., Patel, D., & Shah, M. (2022). A comprehensive study of artificial intelligence and cybersecurity on bitcoin, cryptocurrency and banking system. Annals of Data Science, 11(1), 103-135.

[8] Curzon, J., Kosa, T. A., Akalu, R., & El-Khatib, K. (2021). Privacy and artificial intelligence. IEEE Transactions on Artificial Intelligence, 2(2), 96-108.

[9] Financial Sector Development Program. (2024). Annual report and delivery plan updates under Saudi Vision 2030. Riyadh: Vision 2030.

[10] Ghandour, A. (2021). Opportunities and challenges of artificial intelligence in banking: Systematic literature review. TEM Journal, 10(4), 1581-1587.

[11] Iman, N., Nugroho, S. S., Junarsin, E., & Pelawi, R. Y. (2023). Is technology truly improving the customer experience? Analysing the intention to use open banking in Indonesia. International Journal of Bank Marketing, 41(7), 1521-1549.

[12] Islam Jim, M. M., Hasan, M., & Khatun Munira, M. S. (2024). The role of AI in strengthening data privacy for cloud banking. Frontiers in Applied Engineering and Technology, 1(01), 252-269.

[13] Kaloudi, N., & Li, J. (2020). The AI-based cyber threat landscape: A survey. ACM Computing Surveys, 53(1), 1-34.

[14] Karthik, M. (2024). Cybersecurity threats in banking: Unsupervised fraud detection analysis. International Journal of Science and Research Archive, 11(2), 915-925.

[15] Liu, B., Pavlou, P. A., & Cheng, X. (2022). Achieving a balance between privacy protection and data collection: A field experimental examination of a theory-driven IT solution. Information Systems Research, 33(1), 203-223.

[16] Radanliev, P., & De Roure, D. (2021). Alternative mental models for artificial intelligence privacy and security. IEEE Technology and Society Magazine, 40(3), 37-47.

[17] Radanliev, P., De Roure, D., Nurse, J. R. C., Montalvo, R. M., Cannady, S., Santos, O., & Burnap, P. (2021). Cyber risk from IoT technologies in the supply chain. Sensors, 21(6), 2144.

[18] Saudi Central Bank. (2024). Counter-Fraud Framework. Riyadh: SAMA.

[19] Saudi Central Bank. (2024). Adherence to the Personal Data Protection Law and data governance policies, regulations and rules. Riyadh: SAMA.

[20] Saudi Data and AI Authority. (2024). Personal Data Protection Law and implementing regulatory guidance. Riyadh: SDAIA.

[21] Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe and trustworthy. International Journal of Human-Computer Interaction, 36(6), 495-504.

[22] Snyder, H. (2020). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339.

[23] Tranfield, D., Denyer, D., & Smart, P. (2020). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 31(1), 173-194.

[24] Truby, J., Brown, R., Dahdal, A., & Ibrahim, I. (2020). Blockchain, climate damage, and death: Policy interventions to reduce the carbon emissions, mortality, and net-zero implications of non-fungible tokens and bitcoin. Energy Research & Social Science, 88, 102499.

[25] Vision 2030. (2024). Financial Sector Development Program and national transformation progress. Riyadh: Government of Saudi Arabia.

[26] Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924.

[27] Wang, H., Wang, Y., & Wang, X. (2021). Artificial intelligence in financial fraud detection: A systematic review. Journal of Risk and Financial Management, 14(10), 497.

[28] Xie, L., & Wang, S. (2023). Privacy-preserving machine learning for financial risk control. Expert Systems with Applications, 213, 118957.

[29] Zhang, Y., Li, T., & Liu, Y. (2023). Federated learning for fraud detection in banking: Privacy, performance and governance. IEEE Access, 11, 56631-56645.

[30] Zhou, Z., Chen, X., & Li, Y. (2025). Explainable artificial intelligence for financial crime detection: A review and research agenda. Information Systems Frontiers, 27(1), 155-178.

@article{1717703,
      author = {Malik Ashfaq Ur Rahman},
      title = {AI-Driven Fraud Detection and Financial Security Framework for Saudi Banking Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1652-1662},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717703.pdf},
      abstract = {With the advancement towards immediate payments, mobile account opening, cloud-based systems, open banking and data-driven customer journeys in financial services, fraud in digital banking will become increasingly adaptive. This review paper builds a fraud detection and financial security framework using artificial intelligence (AI) for the banking systems of Saudi Arabia in alignment with Vision 2030. The paper asserts that AI can enhance fraud prevention only by incorporating a coordinated approach to machine learning, data management, human-in-the-loop, regulatory compliance and organizational assurances. By following a structured approach to literature review adopted from some Springer-style and systematic review papers published within the last few years, the paper synthesizes scholarly articles related to the use of AI in fraud detection, anomaly detection, privacy-preserving analytics, model management, and cybersecurity of banking institutions. Based on a comprehensive literature analysis conducted during the period 2020 to 2025, five connected capabilities emerge in terms of developing an effective financial security strategy based on AI: trusted data foundation, fraud intelligence modeling, real-time decision controls, investigation, and escalation, and continuous assurance. Through its literature synthesis process, the paper shows how AI helps detect anomalies through several techniques including graph analytics, behavioral biometrics, NLP, ensemble learning, and XAI. The role of fairness testing, model transparency, privacy control in line with the PDPL, and accountability becomes crucial for achieving success.},
      keywords = {Artificial Intelligence, Fraud Detection, Banking Security, Saudi Banking, Vision 2030, Explainable AI, Financial Crime, Model Governance.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717703}
  }