About this Research Topic
The fintech sector has experienced prolific changes, increasingly relying on digital technologies for implementing asset management and payment systems. This transformation has heightened the vulnerability of both personal and proprietary data against cyber-attacks. Financial institutions amass large quantities of sensitive data that are prone to security risks during storage, processing, or when shared with external entities. Recently, the utilization of artificial intelligence (AI) and machine learning (ML) techniques has been pivotal in counteracting these vulnerabilities by enhancing data management processes within financial systems. This evolving scenario underscores a growing need for sophisticated methods to safeguard financial data effectively.
This Research Topic aims to foster advancements in technologies that safeguard data confidentiality and privacy in the financial arena. By exploring the integration of newer technologies, including diverse digital asset systems, and promoting discussions on AI's role in neutralizing security threats, the research aspires to redefine how privacy and security are perceived and implemented within the financial sector. It strives to showcase cutting-edge machine learning methodologies tailored specifically for financial data analysis, delving into their implications on predictive analytics, strategic planning, and decision-making processes across various domains of finance.
To garner more comprehensive knowledge within the specified realm of interest, this call for papers establishes a framework that encapsulates various aspects of finance integrated with AI capabilities:
• Behavioral patterns and market dynamics through asset pricing and investments
• Regulatory frameworks and compliance issues within corporate finance
• Innovations in blockchain technology and its implications on cryptocurrency markets
• Advanced strategies for ensuring robust cybersecurity and digital asset protection
• Detailed explorations of data privacy laws and their alignment with emerging technologies like EDI (Electronic data interchange) systems and privacy-enhancing mechanisms.
This Research Topic aims to foster advancements in technologies that safeguard data confidentiality and privacy in the financial arena. By exploring the integration of newer technologies, including diverse digital asset systems, and promoting discussions on AI's role in neutralizing security threats, the research aspires to redefine how privacy and security are perceived and implemented within the financial sector. It strives to showcase cutting-edge machine learning methodologies tailored specifically for financial data analysis, delving into their implications on predictive analytics, strategic planning, and decision-making processes across various domains of finance.
To garner more comprehensive knowledge within the specified realm of interest, this call for papers establishes a framework that encapsulates various aspects of finance integrated with AI capabilities:
• Behavioral patterns and market dynamics through asset pricing and investments
• Regulatory frameworks and compliance issues within corporate finance
• Innovations in blockchain technology and its implications on cryptocurrency markets
• Advanced strategies for ensuring robust cybersecurity and digital asset protection
• Detailed explorations of data privacy laws and their alignment with emerging technologies like EDI (Electronic data interchange) systems and privacy-enhancing mechanisms.
Keywords: Artificial intelligence, Blockchain, Cybersecurity, Data Protection, Internet security, Regulatory, Compliance Laws, Security
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
