Forensic AI in Derivatives Markets: Evidentiary Standards for AI-Based Detection Tools in Futures Market Manipulation Enforcement

Abstract

In the era of artificial intelligence (AI), financial market regulators must utilise AI to identify today's more advanced methods of manipulation in futures and derivatives markets. New machine-learning algorithms and anomaly detection methods, graph analytics, and synthetic-content detection systems can now rapidly and accurately detect suspicious trading activity like spoofing, layering, wash trading, and AI-generated manipulation in ways that were previously impossible. The trend of using AI-generated forensic outputs in investigations of market abuse is gaining momentum among regulatory bodies, such as the Commodity Futures Trading Commission (CFTC), the European Securities and Markets Authority (ESMA), and the Securities and Exchange Board of India (SEBI). Even with these technological advances, there is considerable uncertainty in the law regarding the evidentiary value of algorithmic findings. AI systems often provide probabilistic results without clear explanations of the reasoning behind them, which has led to them being called the "black box". This presents difficulties regarding the explainability and reproducibility of the AI model, as well as procedural fairness and the ability to meaningfully contest AI-generated evidence in enforcement actions. This paper is a critical analysis of the novel use of AI-based forensic evidence in enforcement of derivatives market manipulation, including a discussion of the CFTC's requirements for explainability, and considers the effectiveness of current regulatory frameworks, including those in the EU (ESMA) and India (SEBI). The study employs a qualitative doctrinal and comparative legal research approach to examine current regulatory guidance, international principles on AI governance, and recent scholarly work to uncover gaps in evidence. The results indicate that, in the eyes of regulators, explainable AI and audit trails are gaining in significance, but current regulations lack a uniform disclosure requirement regarding algorithmic transparency, model validation, error rates, or independent testing. To overcome these limitations, the paper proposes a framework for Evidentiary Explainability (EEF) with five pillars: algorithmic transparency, reliability assessment, auditability, independent validation, and procedural fairness. The aim of the proposed framework is to enhance the credibility, admissibility, and accountability of forensic evidence produced by artificial intelligence, whilst ensuring effective market oversight and respecting proper due process in the still-evolving field of financial markets.