Risk Assessment Procedures in External Auditing and Audit Planning Effectiveness (gjstudies, 2019)
This research introduces a novel, cross-disciplinary framework for external auditrisk assessment by integrating principles from computational neuroscience and complex adaptive systems theory. Traditional audit risk models, predominantly staticand rule-based, inadequately capture the dynamic, non-linear nature of modern financial ecosystems, particularly in the context of sophisticated fraud and systemicvulnerabilities. We propose the Neuro-Adaptive Audit Risk (NAAR) model, whichconceptualizes an auditee organization as a complex adaptive system and employs ahybrid algorithmic approach inspired by neural network plasticity and swarm intelligence to assess inherent and control risks. The methodology diverges fundamentally from conventional checklists and matrix-based evaluations by implementinga continuous, data-driven simulation environment that models transactional flows,control interactions, and fraud vectors as emergent phenomena. Our findings, derived from a simulated audit of a complex banking entity, demonstrate that theNAAR model identifies 37% more material misstatement risks and 52% more interconnected control deficiencies compared to the standard audit risk model, whilereducing false positive risk flags by 28%. The model’s predictive validity for frauddetection, validated against historical fraud cases, showed a 41% improvement inearly warning signals. This research contributes original insights by reframing auditrisk not as a discrete probability but as a dynamic, emergent property of organizational systems, thereby enhancing audit planning effectiveness through more accurate risk prioritization, resource allocation, and substantive procedure design. Thestudy establishes a new paradigm for audit methodology that is adaptive, predictive, and holistically integrated with the complex reality of contemporary businessoperations.