Posted: Jun 22, 2023
The evolution of audit technology represents a paradigm shift in how organizations approach internal controls and risk management. Traditional audit methodologies, largely unchanged for decades, have increasingly shown limitations in addressing the complexity and velocity of modern business transactions. The conventional audit model, characterized by manual testing, statistical sampling, and periodic reviews, struggles to provide real-time assurance in an era of digital transformation. This research addresses the critical gap between traditional audit practices and the technological capabilities now available to enhance both the efficiency and accuracy of internal audit procedures. Internal audit functions face mounting pressure to deliver greater value while managing expanding regulatory requirements and organizational complexity. The fundamental challenge lies in the inherent limitations of sampling-based approaches, which by design examine only a subset of transactions and controls. This methodological constraint creates unavoidable audit risk and potential oversight of irregular patterns that fall outside sampled populations. Furthermore, the manual nature of traditional audit work introduces human error and subjectivity, while the time-intensive processes often delay the identification and communication of control deficiencies. Our research introduces a novel framework that reimagines internal audit through the lens of integrated technology solutions. We propose that the synergistic combination of artificial intelligence, blockchain technology, and advanced data analytics can transcend the limitations of conventional audit approaches. This integrated model enables continuous monitoring, comprehensive transaction analysis, and immutable evidence collection while preserving the professional judgment that remains essential to the audit process.
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