AI-Driven Approaches to Enhance Cybersecurity in Financial Transactions

Abstract

A surge in digital monetary transactions has resulted in a rise in cyber threats on such platforms. Conventional security measures are slowly eroding and are, therefore, failing to a great extent in curbing these emerging risks. Artificial Intelligence (AI) holds out much promise toward robust cybersecurity through mechanisms with machine learning and anomaly detection techniques, especially natural language processing. This paper tries to explore technical insight into the AI-based framework, approaches, applications, benefits, issues, ethical concerns, and the way forward for the security of financial transactions. Key Words: AI-driven approaches, Cybersecurity, Financial transactions, Machine learning, Natural language processing (NLP), Anomaly detection, Deep learning architectures, Supervised learning, Unsupervised learning, Reinforcement learning, Adversarial machine learning, Data preprocessing, Real-time monitoring, Blockchain integration, Predictive analytics, Explainable AI, Ethical and privacy issues, Regulatory compliance, Quantum computing, Edge AI