Developing an AI-Based Internal Audit Effectiveness Model in Modern Organizations

Abstract

The digital transformation of modern organizations has rendered traditional, cyclical internal audit methodologies insufficient for addressing real-time risks and high-volume data environments. As organizations increasingly rely on automated decision-making systems and complex algorithmic trading, the latency inherent in periodic sampling creates a significant "governance gap." This temporal lag exposes stakeholders to undetected anomalies, fraud, and operational inefficiencies that crystallize before traditional assurance cycles can identify them. This study aims to develop and critically analyze a comprehensive AI-Based Internal Audit Effectiveness Model (AI-IAEM). Utilizing a conceptual research design synthesized from a systematic review of literature published between 2021 and 2026, the research integrates Agency Theory and the Technology-Organization-Environment (TOE) framework to map artificial intelligence capabilities—specifically machine learning, natural language processing, and process mining—against the internal audit lifecycle. The findings present a validated model demonstrating how AI integration shifts internal auditing from a retrospective assurance function to a continuous, predictive governance mechanism. The study contributes to auditing theory by redefining "audit effectiveness" to include algorithmic transparency, "coverage density," and real-time risk coverage, while providing practitioners with a structural blueprint for AI adoption that enhances audit quality, efficiency, and governance oversight. The resulting framework addresses the critical need for a new paradigm where the speed of assurance matches the velocity of risk generation.