Privacy Protection and Compliance of Artificial Intelligence in the Financial Industry
- DOI
- 10.2991/978-94-6463-702-1_3How to use a DOI?
- Keywords
- Artificial Intelligence; Financial Industry; Privacy Protection
- Abstract
This document has explored the intricate relationship between artificial intelligence and privacy protection within the financial industry. It has underscored the significance of adhering to a multi-layered regulatory framework that includes global regulations like the GDPR, industry-specific standards such as PCI DSS, and emerging AI-specific compliance measures. The challenges of data collection and usage, algorithmic bias, and the security of AI systems have been highlighted, emphasizing the need for transparency, ethical data practices, and robust cybersecurity measures. The document concludes that while AI offers transformative potential for financial services, it also necessitates a vigilant approach to privacy protection and compliance. As the financial industry navigates this complex terrain, it must balance innovation with responsibility, ensuring that AI serves to empower rather than exploit, and to protect rather than compromise the privacy rights of individuals.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Surun Mu PY - 2025 DA - 2025/05/05 TI - Privacy Protection and Compliance of Artificial Intelligence in the Financial Industry BT - Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025) PB - Atlantis Press SP - 18 EP - 26 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-702-1_3 DO - 10.2991/978-94-6463-702-1_3 ID - Mu2025 ER -