Predictive Machine Learning Framework for Intelligent Risk Monitoring and Compliance Management in Enterprise Platforms

Abstract

Enterprise platforms increasingly rely on data-driven operations, integrating finance, operations, customer management, and regulatory processes into centralized systems. This complexity exposes organizations to operational, financial, and regulatory risks. Traditional compliance monitoring methods are often reactive, inefficient, and prone to human error, limiting organizations’ ability to identify emerging risks proactively. This research proposes a predictive machine learning (ML) framework for intelligent risk monitoring and compliance management in enterprise platforms. The framework integrates supervised and unsupervised ML models to analyze enterprise data streams, detect anomalies, predict potential risk events, and generate actionable compliance insights. Core components include data ingestion from ERP and CRM modules, feature engineering based on risk sensitivity, predictive modeling, automated alerting, and regulatory compliance mapping. Experimental evaluation on simulated enterprise datasets demonstrates the framework’s ability to enhance risk detection, reduce false positives, and support decision-making in regulatory audits. By providing a proactive, automated approach to risk and compliance management, the framework empowers enterprises to mitigate operational, financial, and legal risks while ensuring regulatory adherence. This research contributes to bridging the gap between predictive analytics and enterprise governance, enabling intelligent, scalable, and secure risk management across modern enterprise platforms