Audit Quality Measurement Indicators and Regulatory Monitoring Applications

Abstract

This research introduces a novel, multi-dimensional framework for measuring audit quality that moves beyond traditional binary compliance metrics. We propose a
hybrid methodology integrating principles from computational linguistics, network theory, and anomaly detection—fields not conventionally applied to audit regulation. Our
approach conceptualizes the audit ecosystem as a dynamic information network, where
quality is emergent from the interactions between audit procedures, evidence, professional judgments, and regulatory feedback. We develop a suite of twelve composite
indicators, including Narrative Coherence Scores derived from audit documentation,
Judgment Convergence Metrics analyzing patterns in professional estimates, and Regulatory Signal Propagation Rates measuring how effectively findings influence firm-wide
practices. A prototype monitoring application was tested using a simulated dataset of
850 audit engagements, generating over 15,000 unique indicator observations. Results
demonstrate that the framework successfully identifies latent quality gradients within
audits that all received passing regulatory inspections, revealing a 40