Redesigning Anti-Manipulation and Anti-Fraud Regulation in Financial Derivatives Markets: Artificial Intelligence Applications in Judicial Precedents

Abstract

Financial derivatives markets play a fundamental role in contemporary financial systems by enabling risk transfer, hedging, speculation, arbitrage, and price discovery across securities, commodities, currencies, interest rates, and energy products. However, the growing complexity, speed, and technological sophistication of these markets have increased their vulnerability to manipulation and fraud. Practices such as spoofing, layering, wash trading, artificial settlement-price pressure, pump-and-dump schemes, benchmark distortion, and algorithmic market abuse create serious challenges for courts and regulators. Traditional judicial approaches usually depend on proving manipulative conduct, intent, causation, artificial price, and investor harm, yet these elements are difficult to establish in markets where misconduct may be distributed across thousands of orders, multiple accounts, high-frequency strategies, communication networks, and cross-market positions. This article examines how artificial intelligence can contribute to redesigning anti-manipulation and anti-fraud regulation in financial derivatives markets by strengthening judicial analysis and improving regulatory enforcement. The study argues that AI can assist courts and regulators through pattern recognition, anomaly detection, order-book analysis, sentiment analysis, network analysis, legal precedent review, and harm calculation. These applications can make hidden manipulative conduct more visible, reconstruct trading sequences, support the inference of intent, estimate artificial price impact, and improve proportionality in sanctions. At the same time, the use of AI in judicial and regulatory contexts must be governed by transparency, explainability, auditability, data reliability, and human oversight. AI should not replace judicial reasoning or due process; rather, it should function as an auxiliary evidentiary and analytical tool. The article concludes that an AI-supported regulatory model can help transform anti-manipulation law from a reactive and fragmented system into a more preventive, integrated, evidence-based, and technically informed framework for protecting the integrity of derivatives markets.