Analyzing the Role of Internal Audit in Supporting Risk Management and Strategic Decision-Making Processes

Posted: Nov 14, 2022

• Melanie Garcia
• Hunter Torres
• Ivy Sanchez

Abstract

This research presents a novel computational framework for analyzing the complex interplay between internal audit functions and organizational risk management systems through the lens of complex adaptive systems theory. Traditional approaches to studying internal audit effectiveness have largely relied on qualitative assessments and linear regression models, failing to capture the dynamic, non-linear relationships that characterize modern organizational environments. Our methodology introduces a hybrid approach combining agent-based modeling, natural language processing of audit documentation, and network analysis to simulate how internal audit activities propagate through organizational decision-making structures. We developed a computational model representing 150 synthetic organizations with varying audit maturity levels, tracking how audit findings influence risk mitigation strategies and strategic choices over a simulated five-year period. The results reveal several counterintuitive findings: internal audit's impact on strategic decision-making follows a threshold effect rather than a linear relationship, with maximum effectiveness occurring at intermediate levels of audit frequency and depth. Additionally, we identified emergent patterns where audit functions acting as 'information brokers' between operational and strategic layers significantly enhanced risk response capabilities. The model also demonstrated that organizations with decentralized audit structures adapted more effectively to environmental shocks than those with traditional centralized models. These findings challenge conventional wisdom about audit centralization and provide a new computational foundation for optimizing audit functions in complex organizational ecosystems. Our approach represents a significant departure from traditional audit research by treating organizations as complex adaptive systems rather than deterministic machines, opening new avenues for computational organizational science.