Securing financial systems through data sovereignty: a systematic review of approaches and regulations

Abstract

Abstract The growing complexity of global financial systems, combined with the digital transformation driven by AI and decentralized technologies, has increased vulnerabilities in data security, privacy, and regulatory compliance. These challenges are particularly dire in the financial sector, where institutions must not only safeguard sensitive financial data but also comply with diverse national and international regulatory frameworks. This study aims to investigate existing approaches for securing financial systems through machine learning, legal frameworks, and regulations, while also highlighting strategies based on decentralization. The review is conducted through the database searches of relevant literature in IEEE Xplore, ACM, ScienceDirect, Springer, and Web of Science. The search covers journals and conferences from 2014 to 2024. Studies not in English and those not addressing the security of financial systems through data sovereignty are excluded. We adopted PRISMA for selecting the final papers for analysis. A total of 741 studies were identified and narrowed down to 52 studies. Following a comprehensive analysis of the legal frameworks and regulatory measures pertaining to artificial intelligence (AI) across various nations and regions, it emerges that the European Union (EU) is at the forefront in this domain. Our in-depth research reveals significant variations in the methodologies employed by different countries to regulate AI and maintain sovereignty over financial data. Of the 13 data sovereignty methods examined, only five explicitly address security concerns, which are viewed as the most important component. Research highlights the challenges of protecting financial systems through data sovereignty in an increasingly digital economy. Although regulatory frameworks, particularly in the EU, have made significant progress in addressing AI-related risks in banking, challenges remain in balancing innovation with regulatory compliance across jurisdictions. The research emphasizes that while AI and machine learning technologies offer significant potential for enhancing financial security, they must operate within robust, harmonized regulatory frameworks to address issues of trust, privacy, and autonomy. Future research should focus on harmonizing global regulatory efforts and exploring ethical considerations to ensure financial systems remain secure, compliant, and inclusive.

Topics & Concepts