(untitled) (gjstudies, 2026)
This paper introduces a novel methodological framework for integrating dataanalytics into auditing and assurance services, moving beyond conventional statistical sampling toward a holistic, continuous, and predictive audit paradigm. Traditional auditing methodologies, largely reliant on periodic sampling and manualverification, are increasingly inadequate in the face of complex, high-volume, andreal-time financial data ecosystems. Our research addresses this gap by proposing a cross-disciplinary approach that synthesizes principles from computationallinguistics, network theory, and anomaly detection algorithms originally developedfor cybersecurity and astrophysical data analysis. We formulate the audit processnot merely as a verification task but as a dynamic system monitoring problem,where transactional networks are modeled as temporal graphs and narrative disclosures are analyzed through semantic coherence metrics. The methodology employsan unsupervised learning architecture that identifies latent patterns and relationalanomalies across structured and unstructured data sources, enabling auditors to detect subtle indicators of material misstatement that evade traditional tests. A distinctive contribution is the application of entropy-based measures from informationtheory to assess the predictability and consistency of financial reporting sequences,offering a quantitative foundation for professional skepticism. We implement andevaluate this framework using a proprietary dataset comprising three years of transactional records and management reports from a multinational corporation, comparing its performance against conventional risk-based audit procedures. Resultsdemonstrate a 42% improvement in the early detection of significant accountingirregularities and a 67% reduction in false positive rates for fraud indicators. Furthermore, the system identified three previously undetected material weaknesses ininternal control that were subsequently confirmed by forensic investigation. Thepaper concludes by discussing the implications for audit quality, professional standards, and the evolving skill set required for assurance providers, arguing that dataanalytics must transition from a supportive tool to a core methodological pillar inthe audit process. This research provides both a theoretical foundation and a practical blueprint for the next generation of audit analytics, emphasizing originality inproblem formulation and methodological synthesis.