The digitization of financial markets has transformed regulatory surveillance through artificial intelligence technologies that monitor billions of daily transactions across global trading venues. These AI-powered systems employ sophisticated techniques including supervised learning, anomaly detection, network analysis, and natural language processing to identify market manipulation more effectively than traditional approaches. While enhancing fraud prevention capabilities for activities like spoofing, front-running, and coordinated trading schemes, these surveillance technologies simultaneously raise profound ethical considerations regarding privacy, data security, algorithmic bias, and potential regulatory overreach. Market participants express concerns about proprietary strategy confidentiality, while regulators face challenges with cross-border data governance and explainability of algorithmic determinations. Technical solutions including federated learning, differential privacy, and explainable AI frameworks are emerging alongside governance mechanisms to balance surveillance effectiveness with ethical considerations, requiring careful implementation to ensure market integrity without impeding innovation or legitimate trading activities.
Keywords: algorithmic bias, differential privacy, explainable AI, federated learning, market surveillance
