Explainable AI (XAI) in Real-Time Trade Reconstruction: Meeting SEC and ESMA Requirements for Algorithmic Trading Oversight
Keywords:
Explainable AI; algorithmic trading; SEC compliance; ESMA regulation; SHAP; LIME; MiFID II; real-time trade reconstruction; market manipulation detection; financial machine learningAbstract
Algorithmic trading has now become the dominant force in today's equity trading markets, but the transparency of their decision-making process is a major problem for regulatory compliance. This paper introduces a novel Explainable Artificial Intelligence (XAI) framework explicitly created for the specific context of real-time trade reconstruction under the Governance requirements of the U.S. Securities and Exchange Commission (SEC) and the European Securities and Markets Authority (ESMA). In our framework, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and attention-based mechanisms are combined in a hybrid machine learning architecture to produce human-interpretable audit trails, compliant with CAT reporting, MiFID II Article 25 and ESMA RTS-6 requirements. Empirically, on a dataset of 2.4M order-level records, the proposed XAI system based on the Transformer outperforms other baseline methods with an accuracy of 92.7%, an AUC-ROC of 0.954, and an explanation latency of less than 36 ms, which meets the compliance requirements for real-time applications. The results show that it is not a requirement for the explainability to reduce detection performance. We have also examined our regulatory coverage in more detail, and find that we meet all seven of the key SEC and ESMA requirements. In this piece, the authors take steps to extend the theory and practice of XAI in high-frequency financial use cases and provide a replicable framework for an algorithmic trading surveillance system that is aligned with regulation.
DOI: https://doi.org/10.17762/ijisae.v9i4.8382
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