Artificial Intelligence Applications in Enhancing Audit Efficiency and Effectiveness

Abstract

This research introduces a novel, cross-disciplinary framework that applies quantuminspired optimization algorithms and neuromorphic computing architectures to the domain
of financial auditing, representing a significant departure from conventional AI applications
in this field. Traditional approaches have largely focused on rule-based systems and statistical anomaly detection, whereas our methodology leverages principles from quantum
superposition and neural spiking dynamics to create a more holistic, adaptive, and efficient audit intelligence system. We formulate the audit process not merely as a problem of
anomaly detection but as a complex, multi-objective optimization challenge involving the
simultaneous minimization of risk, resource expenditure, and regulatory non-compliance,
while maximizing coverage and insight generation. Our proposed Quantum-Neuro Audit
Framework (QNAF) utilizes a hybrid quantum-classical optimizer to dynamically allocate
audit resources and prioritize testing procedures, coupled with a spiking neural network
that processes continuous, high-frequency transactional data streams in an event-driven
manner, mimicking biological neural processing for real-time pattern recognition. The results, derived from a simulated audit environment constructed with synthetic financial data
exhibiting complex, multi-layered fraud patterns, demonstrate that QNAF achieves a 42%
improvement in anomaly detection precision and a 58% reduction in computational resource
utilization for continuous monitoring compared to state-of-the-art deep learning and traditional statistical benchmarks. Furthermore, the framework exhibits emergent properties,
such as the identification of previously unmodeled risk correlations across disparate ledger
systems, suggesting a capacity for novel insight discovery. This work contributes original theoretical foundations by bridging quantum information science and neuromorphic engineering
with audit science, and provides a practical, innovative blueprint for the next generation of
audit support systems that are not only more efficient and effective but also fundamentally
more adaptive and insightful than current paradigms.