An Analysis of the Impact of Audit Materiality Judgments on Financial Statement Accuracy and Fairness

Posted: Jan 28, 2022

• Sienna Ford
• Leo Andrews
• Madelyn Webb

Abstract

This research investigates the complex relationship between audit materiality judgments and the resulting accuracy and fairness of financial statements through a novel computational framework that integrates machine learning with behavioral accounting principles. Traditional approaches to materiality assessment have relied heavily on quantitative thresholds and professional judgment, often overlooking the cognitive biases and contextual factors that influence materiality decisions. Our study introduces an innovative methodology that combines natural language processing of audit documentation with neural network-based pattern recognition to model how materiality judgments evolve during the audit process and subsequently affect financial statement outcomes. We developed a unique dataset comprising 1,250 completed audit engagements from diverse industries, enriched with detailed audit workpaper narratives and subsequent financial restatement data. The research employs a multi-stage analytical approach that first deconstructs materiality judgments into their constituent decision components, then traces how these judgments propagate through the audit process, and finally evaluates their impact on both quantitative accuracy and qualitative fairness dimensions of financial reporting. Our findings reveal several previously undocumented phenomena, including the 'materiality cascade effect' where initial materiality judgments create self-reinforcing patterns throughout the audit, and the 'contextual anchoring bias' whereby auditors' materiality assessments are disproportionately influenced by industry norms rather than entity-specific circumstances. The results demonstrate that conventional materiality thresholds fail to capture approximately 42% of financially significant misstatements due to contextual factors and cognitive biases, while our proposed integrated framework improves detection accuracy by 67%. This research contributes to both accounting theory and practice by providing a comprehensive computational model of materiality judgment formation and its consequences, offering auditors and regulators new tools to enhance financial statement reliability and stakeholder confidence.