Abstract

Organizations managing large volumes of service agreements in life sciences and biotechnology face persistent revenue leakage and compliance failures because critical billing-relevant terms—payment schedules, volume discounts, late-payment penalties, and renewal escalations—are embedded in unstructured legal language. By automating the financial governance of clinical research and supply chain agreements, this framework addresses a critical source of administrative waste that contributes to rising costs in the broader U.S. healthcare system. This paper presents an implementable, human-in-the-loop architecture for contract ingestion, clause segmentation, term extraction, and billing rule generation with full traceability. The system was evaluated on a dataset of 100 expertly curated and densely annotated biotech/clinical research contracts using a 5-fold cross-validation protocol. Results demonstrate: (1) 89.3% precision and 93.1% F1-score in clause classification, (2) 92.0% overall extraction accuracy across five key billing fields, and (3) a 75% reduction in downstream billing error rates compared to manual workflows. The approach combines supervised learning for extraction with deterministic rule-based logic for normalization, ensuring the auditability required for regulated environments.

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