AI-Driven Approaches to Improve Cybersecurity in Financial Transactions

The rapid digitalization of financial services has significantly increased exposure to cyber threats targeting financial transactions. Traditional rule-based cybersecurity mechanisms are insufficient against evolving attack strategies such as fraud, identity theft, adversarial manipulation, and advanced persistent threats. Artificial Intelligence (AI) and Machine Learning (ML) provide adaptive, scalable, and real-time solutions for detecting and mitigating financial cyber risks. This paper presents a comprehensive study of AI-driven approaches for improving cybersecurity in financial transactions. It analyzes supervised, unsupervised, deep learning, graph-based, and reinforcement learning techniques, along with their applications in fraud detection, anti-money laundering (AML), identity verification, and threat intelligence. Implementation frameworks, challenges, ethical implications, and future research directions are also discussed. The results indicate that AI significantly enhances detection accuracy and response efficiency while introducing challenges related to explain ability, privacy, fairness, and adversarial robustness.