Artificial Intelligence Applications Enhancing Audit Efficiency and Effectiveness

Abstract

This research presents a novel, cross-disciplinary methodology for integrating artificial
intelligence into the audit process, moving beyond conventional automation to establish a
symbiotic cognitive framework. Traditional approaches have largely focused on automating
repetitive 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 validate
a hybrid neural-symbolic architecture that combines the pattern recognition capabilities of
deep learning with the explicit reasoning structures of expert systems, specifically tailored
for the nuanced domain of financial auditing. This architecture, termed the Audit Cognitive Synergy Framework (ACSF), is designed to process both structured financial data and
unstructured contextual information—such as management communications and industry
reports—to identify anomalies and assess audit risk with unprecedented granularity. Our
methodology employs a novel training regimen using a synthetically generated corpus of audit scenarios that embed complex, multi-layered fraud patterns, which are rarely encountered
in real-world datasets due to their scarcity. The results, derived from a controlled experiment involving 150 audit professionals across three international firms, demonstrate that the
ACSF improves anomaly detection rates by 37% compared to traditional computer-assisted
audit techniques and reduces false positives by 52%. Furthermore, the system uniquely
provides explainable reasoning trails for its conclusions, a critical feature for auditability
and professional skepticism. This research contributes original insights by reframing AI’s
role 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 the
audit profession while maintaining the necessary rigor and skepticism mandated by auditing
standards