The Effectiveness of Trade Surveillance Models in Identifying Market Manipulation: A Study of Wash Trading and Off-Market Pricing

Abstract

This study explores trade surveillance models especially for wash trading and off-market pricing. These are few fraud behavior patterns that cause persistent threat to market integrity in traditional equity markets, cryptocurrency exchanges and decentralized ecosystems. Although there is growing regulatory attention but there is less improvement in the current surveillance systems and remain fragmented and inconsistent in their ability to uncover manipulative behavior for different market structures. This paper summarizes findings from three peer-reviewed empirical studies to evaluate the effectiveness of current trade surveillance models and proposes an integrated detection framework that combines graph-based network analysis, econometric modeling, machine learning classifiers, and blockchain transparency tools. These reviewed literatures all together demonstrate that: directed graph algorithms achieve more than 95% detection accuracy for collusive wash-trading patterns in traditional regulated markets; around 70% of reported trading volume on certain cryptocurrency platforms is fabricated using coordinated self-transactions; and AI-assisted blockchain analytics can identify wash-trading loops in decentralized NFT markets with more than 95% precision. Researching and studying these findings, this paper proposes a unified, AI-driven surveillance architecture integrating real-time graph traversal, cross-exchange auditing, and blockchain forensic analysis. The framework designed to improve detection speed, reduce false positives, and support regulatory enforcement for both centralized and decentralized financial environments. Paper discusses the implications for regulatory policy, financial compliance infrastructure, and future surveillance system design.