Zero-Shot LLM Evaluation for MiFID II Trading Reports Using Chain-of-Thought Prompting
Keywords:
MiFID II, large language models, zero-shot evaluation, chain-of-thought prompting, regulatory complianceAbstract
This research uses chain-of-thought (CoT) prompting to analyze zero-shot large language model (LLM) evaluation techniques for MiFID II trading reports to increase reasoning transparency and regulatory compliance verification. To evaluate their MiFID II-compliant discrepancies, omissions, and inconsistencies detection, GPT-4 and Gemini algorithms make autonomous logical judgments on trade reporting datasets. To assess model resilience, the research thoroughly tests these LLMs' reasoning trails for coherence, compliance accuracy, and robustness under adversarial testing scenarios using purposely confused or faked trade data. CoT-enabled zero-shot automated regulatory supervision systems' operational correctness strengths and shortcomings are compared using expert-authored validation checklists. LLMs may increase compliance, remove manual review, update transaction reporting, and discover model interpretability and dependability issues. AI-driven reasoning regulatory technology frameworks for financial market compliance automation may benefit from these discoveries.
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