Abstract

LARREA ABAD, Juan Carlos; ABAD SULLON, Yojani Maria  and  CHAMOLI FALCON, Andy Williams. BETWEEN INNOVATION AND REGULATION: A SYSTEMATIC ASSESSMENT OF DATA PRIVACY IN THE FINANCIAL USE OF MACHINE LEARNING. Aula Virtual [online]. 2025, vol.6, n.13, e586.  Epub Dec 27, 2025. ISSN 2665-0398.  https://doi.org/10.5281/zenodo.17945309.

The intensive use of machine learning in the financial sector has transformed the way institutions process and analyze large volumes of data for decision-making, improving efficiency and accuracy in risk and investment management. However, this technological evolution poses serious challenges regarding privacy, personal data protection, and algorithmic accountability, especially in contexts where regulation has not kept pace with innovation. The objective of this study was to assess the privacy implications of using data in machine learning for financial decision-making, with an emphasis on existing regulations and gaps in their application. A systematic review article was developed under the PRISMA 2020 guidelines, encompassing publications indexed in Scopus, Web of Science, and SciELO over the past five years. The results reveal significant gaps in international regulatory harmonization, in the traceability of algorithmic models, and in the application of privacy-enhancing technologies, despite theoretical advances. In conclusion, there is an urgent need for adaptive regulatory frameworks and algorithmic governance that integrates ethics, transparency, and effective data protection in the digital financial ecosystem.

Keywords : Data privacy; machine learning; financial regulation; privacy-enhancing technologies; algorithmic governance.

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