From Controls to Intelligence: A Maturity Model for Analytics-Enabled Compliance Functions

Abstract

Organizations face escalating regulatory complexity that demands a transition from reactive compliance controls to intelligence-driven governance systems. This study introduces the Analytics-Enabled Compliance Intelligence Maturity (AECIM) Framework, a four-stage model Controls, Automation, Analytics, and Intelligence that conceptualizes the structured evolution of compliance capabilities. The framework operationalizes maturity across four interdependent dimensions: processes, technology, people, and culture, emphasizing synchronized transformation rather than isolated technological adoption.

Methodologically, the study employs a conceptual synthesis grounded in systematic literature review and maturity model design principles. To enhance analytical rigor and practical applicability, the framework is supported by a formalized maturity scoring function and an illustrative synthetic validation dataset. A composite maturity index is mathematically defined and applied to simulated organizational data, followed by a simple regression analysis demonstrating a positive association between maturity progression and compliance incident reduction. Complementary visual diagnostics, including a maturity radar profile and impact visualization, provide actionable tools for managerial assessment.

The findings suggest that analytics-enabled maturity is theoretically linked to measurable improvements in compliance effectiveness, repositioning compliance from a cost-centered control function to a predictive, intelligence-based strategic capability. The study contributes a named conceptual framework, an operational assessment mechanism, and an extensible quantitative validation approach, offering both theoretical advancement and practical guidance for organizations pursuing intelligence-driven compliance transformation.