Examining the Role of Internal Control Evaluation in Reducing Audit Risk and Material Misstatements

Posted: Apr 05, 2021

• Amira Diaz
• Theo Patterson
• Lennon Hart

Abstract

This research presents a novel computational framework for internal control evaluation that integrates machine learning algorithms with traditional audit methodologies to quantitatively assess control effectiveness and predict material misstatement risks. Unlike conventional approaches that rely heavily on manual testing and subjective assessment, our methodology employs a multi-layered neural network architecture trained on historical audit data, control testing results, and organizational characteristics to generate probabilistic risk assessments. The framework incorporates natural language processing to analyze control documentation and real-time monitoring data streams to detect control deviations as they occur. Our findings demonstrate that the automated evaluation system can identify control weaknesses with 94.3