Applications of Artificial Intelligence and Machine Learning-based Supervisory Technology in Financial Markets Surveillance: A Review of Literature.

This article systematically reviews the role of Supervisory Technology (SupTech)—which integrates artificial intelligence (AI) and machine learning (ML)—in enhancing financial market supervision, regulation, and surveillance. Based on a systematic literature review of 55 articles from 1999 to 2022 using the PSALSAR framework, it identifies three main application themes: macro prudential forecasting (e.g., bankruptcy, financial distress, economic and market variables), misconduct detection (e.g., fraud, corporate intelligence, risk management), and market surveillance detection (e.g., insider trading and stock price manipulation). The findings highlight that SupTech models, particularly those employing Artificial Neural Networks, Support Vector Machines, and ML algorithms, demonstrate superior predictive accuracy and effectiveness compared to traditional statistical methods, aiding regulators, stock exchanges, intermediaries, and investors in decision-making and risk mitigation. Despite growing adoption by financial authorities and exchanges worldwide, the article notes limited academic research specifically on SupTech's market surveillance applications and calls for expanded studies, especially in diverse geographic markets and emerging financial contexts.