MACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW

Abstract

Audit documentation review represents a critical yet time-intensive component of financial auditing processes, requiring extensive manual analysis of textual evidence, supporting documents, and work papers. Traditional audit documentation review methods rely heavily on manual examination and keyword-based searches, leading to inconsistent coverage, potential oversight of critical issues, and significant resource allocation challenges.

This study proposes a machine learning-enhanced text analytics framework designed to automate and improve the efficiency of audit documentation review processes. The framework integrates Natural Language Processing (NLP) techniques with supervised learning algorithms to automatically classify, prioritize, and extract relevant information from audit documentation. Advanced text mining capabilities enable the identification of risk indicators, compliance issues, and anomalous patterns within large volumes of textual audit evidence.

Experimental validation using real-world audit documentation datasets demonstrates that the proposed framework achieves 91.4% accuracy in document classification and reduces manual review time by 68%. The system successfully identifies high-risk documentation requiring detailed examination while automating the processing of routine audit materials. Implementation results show significant improvements in audit efficiency, consistency, and coverage, supporting enhanced audit quality and regulatory compliance.