Between innovation and regulation: A systematic assessment of data privacy in the financial use of Machine Learning
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Abstract
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
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References
Abdul Salam, M., Fouad, KM, Elbably, DL y Elsayed, SM (2024). Modelo de aprendizaje federado para la detección de fraudes con tarjetas de crédito mediante técnicas de balanceo de datos. Computación neuronal y aplicaciones, 36 (11), 6231-6256.
AbouGrad, H., & Sankuru, L. (2025). Online banking fraud detection model: Decentralized machine learning framework to enhance effectiveness and compliance with data privacy regulations. Mathematics, 13(13), 2110. Documento en linea. Disponible https://doi.org/10.3390/math13132110
Achalapong, P., & Satitsamitpong, N. (2025). Artificial intelligence–driven risk analytics for non-bank financial institutions: Ethical and privacy implications. Journal of FinTech and Data Governance, 12(1), 45–62.
Akash, T., Lessard, D., Reza, N., & Islam, M. (2024). Investigating methods to enhance data privacy in business, especially in sectors like analytics and finance. Journal of Computer Science and Technology Studies, 6(5), 143–151. Documento en linea. Disponible https://doi.org/10.32996/jcsts.2024.6.5.12
Aljunaid, S. K., Almheiri, S. J., Dawood, H., & Khan, M. A. (2025). Secure and transparent banking: Explainable AI-driven federated learning model for financial fraud detection. Journal of Risk and Financial Management, 18(4), 179. Documento en linea. Disponible https://doi.org/10.3390/jrfm18040179
Awosika, T., Shukla, R. M., & Pranggono, B. (2024). Transparency and privacy: the role of explainable ai and federated learning in financial fraud detection. IEEE access, 12, 64551-64560.
Borowicz, M. K. (2024). The data quality problem (in the European Financial Data Space). International Journal of Law and Information Technology, 32, eaae015. Documento en linea. Disponible https://doi.org/10.1093/ijlit/eaae015
Chomczyk Penedo, A., & Trigo Kramcsák, P. (2023). Can the European Financial Data Space remove bias in financial AI development? Opportunities and regulatory challenges. International Journal of Law and Information Technology, 31(3), 253–275. Documento en linea. Disponible https://doi.org/10.1093/ijlit/eaad020
Dasari, S. & Kaluri, R. (2024). Clasificación eficaz de ataques DDoS en una red distribuida mediante técnicas de aprendizaje automático jerárquico y optimización de hiperparámetros. IEEE Access, 12, 10834-10845.
Deshmukh, A., de la Rosa, P. E., Rodríguez, R. V., & Dasari, S. (2025). Enhancing privacy in IoT-enabled digital infrastructure: Evaluating federated learning for intrusion and fraud detection. Sensors, 25(10), 3043. Documento en linea. Disponible https://doi.org/10.3390/s25103043
Dhabliya, D., Rizvi, N., Dhablia, A., Sridhar, A., Kale, S., & Padhi, D. (2024). Securing machine learning ecosystems: Strategies for building resilient systems. E3S Web of Conferences, 491, 02033. Documento en linea. Disponible https://doi.org/10.1051/e3sconf/202449102033
El-Taj, H., Hamedah, D., & Saeed, R. (2025). Artificial intelligence and advanced cybersecurity to mitigate credential-stuffing attacks in the banking industry. International Journal of Computational and Experimental Science and Engineering (IJCESEN), 11(1), 935–948. Documento en linea. Disponible https://doi.org/10.22399/ijcesen.754
Hamed, T. (2023). Navigating the ethical and privacy concerns of big data and machine learning in decision making. Intelligent and Converged Networks, 4(4), 280–295. Documento en línea. Disponible https://doi.org/10.23919/icn.2023.0023
Hockstad, T., Rahman, M., Jones, S., & Chowdhury, M. (2024). A regulatory gap analysis in transportation cybersecurity and data privacy. Transportation Journal, 64(1). Documento en linea. Disponible https://doi.org/10.1002/tjo3.12036
Kanamori, S., Abe, T., Ito, T., Emura, K., Wang, L., Yamamoto, S., Phong, L. T., Abe, K., Kim, S., Nojima, R., Ozawa, S., & Moriai, S. (2022). Privacy-preserving federated learning for detecting fraudulent financial transactions in Japanese banks. Journal of Information Processing, 30, 789–795. Documento en linea. Disponible https://doi.org/10.2197/ipsjjip.30.789
Kong, L., Luo, Y., Abidian, MR, Ahn, JH, Akinwande, D., Andrews, AM, Antonietti, M., ... y Chen, X. (2023). Hoja de ruta tecnológica para sensores flexibles. ACS nano, 17 (6), 5211-5295.
Lee, J. (2020). Access to finance for artificial intelligence regulation in the financial services industry. European Business Organization Law Review, 21, 731–757. Documento en linea. Disponible https://doi.org/10.1007/s40804-020-00200-0
Li, M., & Walsh, J. (2024). FEDGAT-DCNN: Advanced credit card fraud detection using federated learning, graph attention networks, and dilated convolutions. Electronics, 13(16), 3169. https://doi.org/10.3390/electronics13163169
Liu, Z., Guo, J., Lam, K., & Zhao, J. (2023). Efficient dropout-resilient aggregation for privacy-preserving machine learning. IEEE Transactions on Information Forensics and Security, 18, 1839–1854. Documento en línea. Disponible https://doi.org/10.1109/tifs.2022.3163592
Mohammed, S., Osman, Y., Ibrahim, A., & Shaban, M. (2025). Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review. International Nursing Review, 72(1). Documento en linea. Disponible https://doi.org/10.1111/inr.70010
Nadella, G. S., Gonaygunta, H., Harish, M., & Whig, P. (2025). Privacy and security: safeguarding personal data in the AI era. In Ethical dimensions of AI development (pp. 157-174). IGI Global.
Ogundele, A. T., Ibitoye, O. A., Akinterinwa, O. O., Abraham, A., Ibukun, F. O., & Apata, T. G. (2025). The role of artificial intelligence in advancing sustainable banking and service efficiency in Nigerian financial institutions: An assessment of selected quoted banks. The Journal of Sustainable Development Law and Policy, 16(1), 282–307. Documento en linea. Disponible https://doi.org/10.4314/jsdlp.v16i1.15
Oualid, A., Maleh, Y., & Moumoun, L. (2023). Federated learning techniques applied to credit risk management: A systematic literature review. EDPACS, 68(1), 42-56.
Patil, A., Mishra, B., Chockalingam, S., Misra, S., & Kvalvik, P. (2025). Securing financial systems through data sovereignty: A systematic review of approaches and regulations. International Journal of Information Security, 24, 159. Documento en linea. Disponible https://doi.org/10.1007/s10207-025-01074-4
Pereira, D., Reis, P. R., & Borges, F. (2024). Secure aggregation protocol based on DC-Nets and secret sharing for decentralized federated learning. Sensors, 24(4), 1299. Documento en linea. Disponible https://doi.org/10.3390/s24041299
Qi, T., Wu, F., Wu, C., He, L., Huang, Y., & Xie, X. (2023). Differentially private knowledge transfer for federated learning. Nature Communications, 14(1). Documento en linea. Disponible https://doi.org/10.1038/s41467-023-38794-x
Selvamani, T., Arjunan, P., & Poovammal, E. (2025). Intelligent mortgage optimization: Leveraging AI for personalized lending and risk assessment. International Journal of Basic and Applied Sciences, 14(2), 113–118. Documento en linea. Disponible https://doi.org/10.14419/psrwrt75
Szepannek, G., & Lübke, K. (2021). Facing the challenges of developing fair risk scoring models. Frontiers in Artificial Intelligence, 4, 681915. Documento en linea. Disponible https://doi.org/10.3389/frai.2021.681915
Talukder, K., & Shompa, T. (2024). Artificial intelligence in criminal justice management: A systematic literature review. NHJ, 1(01), 63–82. Documento en linea. Disponible https://doi.org/10.70008/jmldeds.v1i01.42
Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. (2023). Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms. Electronics, 12(8), 1789. Documento en linea. Disponible https://doi.org/10.3390/electronics12081789
Vecchietti, G., Liyanaarachchi, G., & Viglia, G. (2025). Managing deepfakes with artificial intelligence: Introducing the business privacy calculus. Journal of Business Research, 186, 115010. Documento en linea. Disponible https://doi.org/10.1016/j.jbusres.2024.115010
Wirth, F., Meurers, T., Johns, M., & Praßer, F. (2021). Privacy-preserving data sharing infrastructures for medical research: Systematization and comparison. BMC Medical Informatics and Decision Making, 21(1). Documento en linea. Disponible https://doi.org/10.1186/s12911-021-01602-x
Yuspin, W., Afnan, H. A., Wardiono, K., Budiono, A., Prakoso, A. L., Rajput, T., Basu Bal, A., & Pitaksantayothin, J. (2025). Deep fakes in P2PL services: Assessing legal challenges and data privacy risks. WSEAS Transactions on Computer Research, 13(43), 469–482. Documento en linea. Disponible https://doi.org/10.37394/232018.2025.13.43
Yuxin, M., & Honglin, W. (2023). Federated Learning Based on Data Divergence and Differential Privacy in Financial Risk Control Research. Computers, Materials & Continua, 75(1).
Zhang, J. (2022). Dynamic audit of internet finance based on machine learning algorithm. Mobile Information Systems, 2022, 1–12. Documento en linea. Disponible https://doi.org/10.1155/2022/7072955