Detecting and preventing premium billing variances in ASO/ASC Health Plans: A controls-first analytics framework for eligibility-to-invoice integrity

The research presented here introduces a Controls-First Analytics Framework to minimize premium billing variance in Administrative Services Only (ASO) and Administrative Services Contract (ASC) health insurance plans. Healthcare financial ecosystems, with their asynchronous data processing between employer Human Resource Information Systems (HRIS), Third-Party Administrators (TPAs) and carrier billing systems, often undermine the quality of the eligibility-to-invoice process (Patabendige & Hopkins, 2025). Current detective controls, with their emphasis on ex-post audits and post-payment reconciliation, are reactive and lead to financial losses, inefficient processing, and poor audit trail (Ogedengbe et al., 2022).
This research proposes a proactive model with controls integrated in the data stream. The framework comprises three main components: (1) Algorithmic Eligibility Validation (AEV) at data input, (2) automated transaction reconciliation mechanisms and (3) variance classification using a variance taxonomy. This methodology is in line with new research on financial reconciliation and continuous auditing, which show substantial gains in accuracy and efficiency (Khan & Mita, 2024).
The architecture is tested using operational data simulations, as well as failure scenarios such as retrospective terminations, tier mapping discrepancies and demographic eligibility errors. The framework is evaluated in terms of Variance Reduction Rate (VRR) and Mean Time to Resolution (MTTR). Results show significant reductions in the number of cycles required to resolve exceptions, and improved audit preparedness through automated, non-repudiable documentation (Karagoz, 2025).
The research offers a systems-based, scalable approach to enhance financial process integrity in health care billing, especially in resource restricted and high-volume administrative operating models.