(untitled) (gjstudies, 2024)
This research introduces a novel, multi-dimensional framework for measuring audit quality that moves beyond traditional binary compliance metrics. We propose ahybrid methodology integrating principles from computational linguistics, network theory, and anomaly detection—fields not conventionally applied to audit regulation. Ourapproach conceptualizes the audit ecosystem as a dynamic information network, wherequality is emergent from the interactions between audit procedures, evidence, professional judgments, and regulatory feedback. We develop a suite of twelve compositeindicators, 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-widepractices. A prototype monitoring application was tested using a simulated dataset of850 audit engagements, generating over 15,000 unique indicator observations. Resultsdemonstrate that the framework successfully identifies latent quality gradients withinaudits that all received passing regulatory inspections, revealing a 40