Abstract

In an era of escalating regulatory complexity and costly compliance breaches, this paper proposes a predictive compliance analytics framework that harnesses artificial intelligence and business intelligence tools to enable early risk detection in financial and insurance sectors. By integrating machine learning models with interactive dashboard platforms such as Tableau, SQL, and Python, the framework transforms traditional reactive compliance approaches into a proactive, data-driven system. The framework incorporates diverse data sources, advanced algorithms, and real-time visualization to identify anomalies, predict non-compliance events, and support timely interventions. Implementation scenarios demonstrate its effectiveness in detecting anti-money laundering violations, fraud, and cybersecurity threats. Evaluation criteria highlight significant improvements in prediction accuracy, cost reduction, and response efficiency. The study concludes with implications for compliance officers, IT leaders, and regulators, while addressing limitations related to data quality, model bias, and scalability. Future research directions emphasize deep learning integration and cross-sector applicability to enhance regulatory adherence and governance.