Authors: Srinivasa Chakravarthy Seethala
Abstract: This study proposes an AI driven compliance intelligence platform designed to enable continuous monitoring and automated risk assessment within highly regulated CRM and ERP environments. Organizations operating in finance, healthcare, public sector, and other compliance intensive domains increasingly rely on complex enterprise platforms where regulatory obligations evolve faster than traditional audit and control mechanisms can adapt. The research addresses the limitations of static compliance models by introducing an architecture that integrates machine learning, natural language processing, and policy aware analytics to interpret regulatory requirements, monitor transactional and configuration level signals, and dynamically assess compliance risk in real time. A mixed methodological approach is adopted, combining conceptual system design with simulated enterprise data flows and scenario based evaluations across common regulatory regimes such as data protection, financial controls, and access governance. The findings demonstrate that AI driven compliance intelligence can significantly improve early risk detection, reduce manual audit effort, and enhance traceability across CRM and ERP processes by continuously correlating system behavior with regulatory intent. The platform introduces adaptive risk scoring, automated control validation, and explainable compliance insights that support both operational teams and governance stakeholders. From a strategic perspective, the study contributes to a forward looking compliance paradigm that shifts organizations from periodic, reactive audits toward proactive and continuous assurance models. Academically, the research extends existing literature on enterprise governance by formalizing compliance intelligence as a scalable, data driven capability embedded within enterprise software ecosystems.
DOI: http://doi.org/10.5281/zenodo.17938605
