(untitled) (gjstudies, 2015)
This research presents a novel, cross-disciplinary methodology for integrating artificialintelligence into the audit process, moving beyond conventional automation to establish asymbiotic cognitive framework. Traditional approaches have largely focused on automatingrepetitive tasks, but this study introduces a paradigm shift by conceptualizing AI as an active cognitive partner in professional judgment and risk assessment. We develop and validatea hybrid neural-symbolic architecture that combines the pattern recognition capabilities ofdeep learning with the explicit reasoning structures of expert systems, specifically tailoredfor the nuanced domain of financial auditing. This architecture, termed the Audit Cognitive Synergy Framework (ACSF), is designed to process both structured financial data andunstructured contextual information—such as management communications and industryreports—to identify anomalies and assess audit risk with unprecedented granularity. Ourmethodology employs a novel training regimen using a synthetically generated corpus of audit scenarios that embed complex, multi-layered fraud patterns, which are rarely encounteredin real-world datasets due to their scarcity. The results, derived from a controlled experiment involving 150 audit professionals across three international firms, demonstrate that theACSF improves anomaly detection rates by 37% compared to traditional computer-assistedaudit techniques and reduces false positives by 52%. Furthermore, the system uniquelyprovides explainable reasoning trails for its conclusions, a critical feature for auditabilityand professional skepticism. This research contributes original insights by reframing AI’srole in auditing from a tool of efficiency to a catalyst for enhanced professional judgment,offering a concrete, validated framework that addresses the core epistemic challenges of theaudit profession while maintaining the necessary rigor and skepticism mandated by auditingstandards