EU AI Act Compliance Framework for Financial Institutions: A Knowledge-Driven Approach

Cheah Wei Lun, Eric (2025) EU AI Act Compliance Framework for Financial Institutions: A Knowledge-Driven Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The European Union's Artificial Intelligence Act presents compliance challenges for financial institutions, with penalties reaching €35 million or 7% of global annual turnover. This research develops a proof-of-concept expert-augmented decision support framework addressing implementation gaps between regulatory requirements and practical guidance within financial services. Using Design Science Research methodology, this study analysed EU Regulation 2024/1689 and developed a three-component modular architecture: a Regulatory Knowledge Engine organising 12 core regulatory articles, an Expert Guidance Interface providing role-specific navigation across technical teams, compliance officers, legal departments, and senior management, and a Documentation Assistant offering template validation through simulation-based assessment workflows. The web-based implementation demonstrates structured regulatory knowledge organisation through single-file HTML architecture while maintaining clear acknowledgement of proof-of-concept limitations including visual document simulation and pre-configured institutional scenarios. Commercial viability assessment using publicly available institutional data demonstrates protection ratios across different organisational scales: Allied Irish Banks (16.1x-36.1x), Banco Santander (72.5x-108.8x), and ING Group (39.5x-63.2x), providing investment justification under stated modelling assumptions. This research contributes expert-augmented RegTech frameworks that maintain human expertise requirements while providing structured implementation guidance for the August 2026 compliance deadline. Future research requires stakeholder validation, production-level development, and effectiveness assessment for institutional implementation.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Del Rosal, Victor UNSPECIFIED
Uncontrolled Keywords:	EU AI Act; financial services compliance; RegTech; expert-augmented decision support; Design Science Research
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence J Political Science > JN Political institutions (Europe) > European Union H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Ciara O'Brien
Date Deposited:	20 Aug 2026 10:33
Last Modified:	20 Aug 2026 10:33
URI:	https://norma.ncirl.ie/id/eprint/9567

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