AI-Based Security Engineering for Fintech and Regulated Industries
Annotatsiya
Fintech and other highly regulated sectors that face growing cyber threats, compounding compliance requirements and even faster digital change are now dependent on AI-based security engineering. The more basic enabling technologies, including machine learning algorithms, systems design, and automated compliance integration, are discussed in this review, and how AI can be used to detect threats, score risk, and react to incidents where false positives and latency are the key elements. It provides a comparison between classical and deep learning architecture, describing the architectures that adopt a mixture of CNNs, LSTMs, and other technologies to provide accuracy and robustness in intrusion detection. It proposes a novel multi-tier security model, which dwells upon the feedbackbased learning, regulation compliance, and adversarial resistance. The key research gaps such as explainable AI, federated learning in order to maintain privacy and policy-conscious response to threats are described. The article unites both theoretical and experimental results, along with the solutions to the technological and regulatory problems.