Governance, Risk, and Compliance (GRC) Frameworks Using Intelligent Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806016Keywords:
Governance Risk and Compliance, Intelligent Analytics, Artificial Intelligence, Machine Learning, Risk Management, Regulatory Compliance, Data-Driven Decision Making, Predictive Analytics, Enterprise Governance, Fraud Detection ModelsAbstract
Governance, Risk, and Compliance (GRC) frameworks are essential mechanisms through which organizations align strategic objectives with regulatory requirements, risk management practices, and ethical governance. As organizations increasingly operate in complex, data-rich, and highly regulated environments, traditional GRC approaches—often manual, siloed, and reactive—are proving insufficient. Intelligent analytics, powered by advances in artificial intelligence (AI), machine learning (ML), big data analytics, and predictive modeling, is transforming GRC frameworks by enabling proactive, integrated, and real-time decision-making.
This study explores the role of intelligent analytics in enhancing modern GRC frameworks, focusing on how data-driven techniques improve governance effectiveness, risk identification, and regulatory compliance. Intelligent analytics enables organizations to process vast volumes of structured and unstructured data from internal systems, external regulatory sources, and operational environments. By applying advanced algorithms, organizations can identify emerging risks, detect anomalies, forecast compliance breaches, and assess governance performance with greater accuracy and speed than conventional methods.
In governance, intelligent analytics supports transparency, accountability, and strategic alignment by providing actionable insights into organizational performance, policy adherence, and decision outcomes. In risk management, predictive and prescriptive analytics facilitate early risk detection, continuous monitoring, and dynamic risk scoring, allowing organizations to shift from reactive mitigation to proactive prevention. In compliance management, intelligent analytics automates regulatory mapping, monitors control effectiveness, and ensures timely adherence to evolving laws and standards, reducing compliance costs and human error.
The integration of intelligent analytics within GRC frameworks also promotes a unified view of organizational risk and compliance, breaking down functional silos and enabling enterprise-wide visibility. However, challenges such as data quality issues, algorithmic bias, lack of explainability, cybersecurity threats, and regulatory concerns around AI governance remain critical considerations. Addressing these challenges requires robust data governance, ethical AI practices, and alignment with international standards.
This abstract concludes that intelligent analytics significantly enhances the effectiveness, agility, and resilience of GRC frameworks. By embedding advanced analytics into GRC processes, organizations can achieve improved risk foresight, stronger regulatory compliance, and more informed governance decisions. The study highlights the need for continuous innovation, cross-functional collaboration, and responsible AI adoption to fully realize the potential of intelligent analytics in next-generation GRC frameworks.
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