Auditing Healthcare Claims Through Large-Scale NLP-Based Consistency and Compliance Checks

Abstract

This paper presents a detailed investigation into automated methods for auditing healthcare claims by leveraging large-scale natural language processing pipelines that check for consistency and compliance across clinical documentation and financial data. The primary focus is to harness advanced text processing models to identify anomalies, verify adherence to medical billing standards, and detect potential fraud or misrepresentation in patient claims. We discuss an integrative strategy that combines linguistic embeddings of clinical narratives, ontological representations of medical coding rules, and algorithmic checks for insurance regulations to identify possible deviations. A central concern is to ensure that the interpretability of computational models remains intact while handling high-dimensional data spanning numerous claim types, patient histories, and regulatory frameworks. We provide theoretical perspectives on how such models can be optimized and validated in practice, particularly in large institutions that process vast volumes of insurance claims daily. Our proposed approach relies on algorithmic detection of semantic contradictions, symbolic logic checks for constraints, and computationally efficient transformations of input data to handle variable lengths of clinical text. Experimental analyses demonstrate how this pipeline can mitigate inconsistencies without introducing excessive computational overhead. By consolidating the latest breakthroughs in automated text processing and healthcare compliance, this work contributes an interdisciplinary perspective on accurate, transparent, and scalable auditing of medical claims.