(untitled) (gjstudies, 2024)
This research investigates the relationship between contemporary audit risk assessmentmethodologies and the accuracy of subsequent audit planning decisions. While traditionalaudit frameworks emphasize standardized risk evaluation procedures, emerging complexities in financial systems, technological integration, and regulatory environments demandmore adaptive and nuanced approaches. This study posits that the accuracy of audit planning—defined as the precise allocation of resources, timing, and procedures to address identified risks—is significantly influenced not merely by the identification of risks, but by themethodological depth and contextual intelligence embedded within the assessment phase.We introduce and evaluate a novel, multi-dimensional risk assessment framework that integrates continuous data analytics, behavioral factors of auditee management, and systemicintegrity indicators derived from information systems auditing principles. The methodologyemploys a quasi-experimental design, comparing audit plans generated using the proposedframework against those derived from conventional checklists and matrix-based models ina series of simulated audit engagements involving complex transactional environments, including those with potential anti-money-laundering (AML) complexities. Results indicatea statistically significant improvement in planning accuracy, measured by the alignmentbetween planned procedures and subsequently revealed material misstatements or controlfailures, when using the integrated framework. The findings contribute original insights bydemonstrating that moving beyond binary, compliance-focused risk scoring toward a dynamic, evidence-assimilative assessment process directly enhances the strategic precision ofaudit engagements. This has profound implications for audit efficiency, fraud detection efficacy, and the overall reliability of financial reporting, particularly in domains where systemcontrols and data integrity are paramount