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Secure Intelligence in Banking: An Integrated Framework for Artificial Intelligence with Data Privacy and Security Preservation | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 26, Special 2, February 2026, Pages 22-34 PDF (1.09 M) | ||
| Document Type: Research Paper | ||
| DOI: DOI://doi.org/10.33916 | ||
| Authors | ||
| hayder makki shakir1; Hadir H Shubbar2 | ||
| 1Faculty of Computer Science and Information Technology, University of Al-Qadisiyah, Qadisiyah, Iraq | ||
| 2Faculty of Administration and Economics-Department of Banking and Finance-University of AL- Qadisiya , Republic of Iraq. | ||
| Abstract | ||
| AI is being deployed to transform digital banking, allowing us to move beyond just predictive analytics and fraud detection. But when they implement AI on a scale, they expose sensitive financial information to privacy, security, and compliance breaches. This paper introduces a Secure Intelligence Architecture (SIA). 1 HE, SMPC, DP, and TEE-Based Conceptual Framework Our conceptual framework is a combination of Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Trusted Execution Environments that are used to guarantee end-to-end confidentiality and privacy compliance. Composed of four interdependent fractal layers (Data Governance, Privacy & Cryptography, AI Intelligence, and Compliance & Monitoring), the Academy Framework establishes trust as a continuous, flowing process throughout the entire data lifecycle. According to the theory, the SIA reduces exposure risk and enhances compliance through automated RegTech auditing. A model based on a ‘fake algorithm’ and its formal arithmetic establishes that we can have secure computation without compromising the usability. The research presents an example for building scalable, trustworthy AI in financial ecosystems . | ||
| Keywords | ||
| Artificial Intelligence; Secure Banking; Homomorphic Encryption; Differential Privacy; Trusted Execution Environments; Computational Trust; RegTech; Data Governance; and Privacy-Preserving Analytics | ||
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