AI-Driven Financial Security: Innovations in Protecting Assets and Mitigating Risks

The financial sector encounters numerous challenges such as cyber threats, fraud, and regulatory compliance. Traditional methods of safeguarding financial transactions and assets are becoming increasingly insufficient against advanced cyber-attacks. This thesis examines the transformative impact of Artificial Intelligence (AI) on financial security. It investigates various AI-driven innovations, their applications in asset protection, and risk mitigation, while also considering the ethical and regulatory implications. AI is reshaping financial risk management by offering advanced tools and techniques for identifying, assessing, and mitigating risks. This article explores the innovations and applications of AI-driven financial risk management, emphasizing its transformative effect on traditional risk management practices. We discuss various Artificial intelligence technology, such as natural language processing, predictive analytics, and machine learning and their applications in enhancing financial stability, regulatory compliance, and operational efficiency. As cyber threats grow more sophisticated, traditional network security approaches are becoming inadequate due to scalability issues, slow response times, and the inability to detect advanced threats. This highlights the need for research into more efficient security methods to protect against diverse network attacks. Cybercriminals use AI for data poisoning and model theft to automate attacks, emphasizing the need for AI-based cybersecurity techniques. This study introduces a cybersecurity technique based on AI for financial sector management (CS-FSM) to map and prevent unforeseen risks. By utilizing AI technologies like the K-Nearest Neighbor (KNN) algorithm with the Enhanced Encryption Standard (EES), the suggested approach improves data privacy, scalability, risk reduction, data protection, and attack avoidance, significantly improving the performance of cybersecurity systems in the financial sector.