Data privacy in banking faces several challenges, including the use of nontraditional data in financial forecasting, ethical and privacy implications, data quality, financial stability and systemic risk, crime, national security, and regulatory frameworks. The use of AI/ML in forecasting offers benefits but also poses challenges, especially when using nontraditional data such as social media data, browsing history, and location data in financial forecasting. This raises concerns about the governing legal and regulatory framework, ethical and privacy implications, and data quality in terms of cleanliness, accuracy, relevancy, and potential biases .
Advances in digital and distributed ledger technology for financial services have led to dramatic growth in markets for digital assets, with profound implications for data privacy and security, financial stability and systemic risk, crime, national security, and financial inclusion and equity. This growth has implications for the protection of consumers, investors, and businesses, including data privacy and security. Monetary authorities globally are also exploring, and in some cases introducing, central bank digital currencies (CBDCs) .
The adoption of cloud-based technology is discussed as a potential solution for better patient data archiving and usage, lower storage costs, quicker innovation cycles, more straightforward collaboration, and increased telemedicine possibilities. However, this also raises concerns about security vulnerabilities and data privacy .
Privacy protection and cyber security are interconnected, and challenges for cyber security are also challenges for privacy and data protection. Effective cyber security implementation by organizations is crucial to secure personal data both when it is in transit and at rest .