Assessing the Effectiveness of Audit Sampling Methods in Detecting Material Misstatements in Complex Financial Statements

Posted: Jan 12, 2020

• Lila Armstrong
• Fiona Myers
• Evie Martinez

Abstract

This research investigates the efficacy of traditional audit sampling methodologies in detecting material misstatements within increasingly complex financial statements characterized by high-volume transactions, intricate financial instruments, and sophisticated fraud schemes. Traditional audit sampling approaches, including monetary unit sampling and classical variables sampling, were developed in an era of predominantly manual accounting systems and may not adequately address the challenges posed by modern financial reporting environments. This study introduces a novel hybrid sampling framework that integrates machine learning-based anomaly detection with traditional statistical sampling methods to enhance the detection of material misstatements. The methodology employs a multi-stage approach combining unsupervised learning algorithms for initial risk stratification with adaptive sampling techniques that dynamically adjust sampling parameters based on detected patterns of irregularity. Our analysis of simulated financial data representing complex multinational corporations demonstrates that the hybrid approach achieves a 42