Solutions for Insider Trading and Regulatory Challenges in Financial Governance
DOI:
https://doi.org/10.56294/dm2025680Keywords:
Insider Trading Detection, AI in Financial Governance, Regulatory Compliance, Market Stability, Decentralized Financial SystemsAbstract
Insider trading and regulatory inconsistencies have important historical challenges to the integrity and stability of global financial markets. These issues challenge trust, transparency, and fairness are requiring solutions. In this study, we introduce a novel artificial intelligence (AI)-driven system that carefully addressing these challenges. The proposed system employs machine learning models for insider trading detection, natural language processing (NLP) for sentiment analysis, and graph neural networks (GNNs) to detect irregular patterns in blockchain transactions. Moreover, reinforcement learning techniques are utilized here to complement regulatory standards dynamically, enhancing policy flexibility and market agreement. Explainable AI (XAI) were used here as well to ensure the transparency and trust in decision-making processes, this helps stakeholders to take actions. Experimental evaluations prove the system efficiency, with promising precision and recall percentages, enhanced governance in decentralized systems, and robust cross-jurisdictional regulatory alignment. This research contributes to knowledge by proving the transformative prospective of AI in strengthening regulatory frameworks and improving governance mechanisms in financial systems. The achievements here provide a roadmap for policymakers, financial institutions, and technology developers to build reasonable, efficient, and resistant markets.
El tráfico de información privilegiada y las inconsistencias regulatorias han sido desafíos históricos importantes para la integridad y estabilidad de los mercados financieros globales. Estos problemas desafían la confianza, la transparencia y la equidad y requieren soluciones. En este estudio, presentamos un nuevo sistema impulsado por inteligencia artificial (IA) que aborda cuidadosamente estos desafíos. El sistema propuesto emplea modelos de aprendizaje automático para la detección de tráfico de información privilegiada, procesamiento del lenguaje natural (NLP) para el análisis de sentimientos y redes neuronales gráficas (GNN) para detectar patrones irregulares en transacciones de blockchain. Además, aquí se utilizan técnicas de aprendizaje de refuerzo para complementar los estándares regulatorios de forma dinámica, mejorando la flexibilidad de las políticas y el acuerdo del mercado. Aquí también se utilizó IA explicable (XAI) para garantizar la transparencia y la confianza en los procesos de toma de decisiones, lo que ayuda a las partes interesadas a tomar medidas. Las evaluaciones experimentales prueban la eficiencia del sistema, con porcentajes prometedores de precisión y recuperación, una gobernanza mejorada en sistemas descentralizados y una sólida alineación regulatoria interjurisdiccional. Esta investigación contribuye al conocimiento al demostrar la perspectiva transformadora de la IA en el fortalecimiento de los marcos regulatorios y la mejora de los mecanismos de gobernanza en los sistemas financieros. Los logros aquí alcanzados proporcionan una hoja de ruta para que los responsables de las políticas, las instituciones financieras y los desarrolladores de tecnología construyan mercados razonables, eficientes y resistentes.
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Copyright (c) 2025 Haneen A. Al-khawaja, Abdul Razzak Alshehadeh, Faisal Asad Aburub, Ali Matar, Osaid Hasan Althnaibat (Author)
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