[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125323-en":3,"doc-seo-125323-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125323,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","MACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW - read online free","Audit documentation review is a time-intensive core activity in financial auditing, requiring extensive manual analysis of textual evidence, supporting documents, and work papers. Traditional workflows depend on manual reading and keyword-based searches, which can lead to inconsistent coverage, missed critical issues, and heavy resource use. A machine learning-enhanced text analytics framework is proposed to automate classification, prioritization, and extraction, using NLP and supervised learning. Experiments on real audit datasets report 91.4% accuracy and a 68% reduction in manual review time, improving audit efficiency, consistency, and compliance.","Central Lancashire Online Knowledge (CLoK)  \n\n| Title | MACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW |\n| --- | --- |\n| Type | Article |\n| URL | [https://clok.uclan.ac.uk/id/eprint/56682/](https://clok.uclan.ac.uk/id/eprint/56682/) |\n| DOI | doi:10.61784/jtfe3056 |\n| Date | 2025 |\n| Citation | Reid, Matthew, Stone, Julia and Whittaker, Paul (2025) MACHINE LEARNINGENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW. Journal of Trends in Financial and Economics, 2 (3) . pp. 55-62. ISSN 3007-6951 |\n| Creators | Reid, Matthew, Stone, Julia and Whittaker, Paul |\n\nIt is advisable to refer to the publisher’s version if you intend to cite from the work. doi:10.61784/jtfe3056  \nFor information about Research at UCLan please go to [http://www.uclan.ac. uk/research/](http://www.uclan.ac. uk/research/)  \n[All outputs in CLoK are protected by Intellectual Property Rights law](All outputs in CLoK are protected by Intellectual Property Rights law), including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the  \n[http://clok.uclan.ac.uk/policies/](http://clok.uclan.ac.uk/policies/)  \nJournal of Trends in Financial and Economics  \nPrint ISSN: 3007-6951  \nOnline ISSN: 3007-696X  \nDOI:[https://doi.org/10.61784/jtfe3056](https://doi.org/10.61784/jtfe3056)  \nMACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW  \nMatthew Reid, Julia Stone, Paul Whittaker*  \nSchool ofBusiness, University ofCentral Lancashire, Lancashire, UK. Corresponding Author: Paul Whittaker, [Email: paul.whittaker5721@gmail.com](Email: paul.whittaker5721@gmail.com)  \nAbstract: 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.  \nThis 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.  \nExperimental 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.  \nKeywords: Audit documentation; Text analytics; Machine learning; Natural Language Processing (NLP); Audit efficiency; Risk assessment; Document classification; Audit automation  \n1 INTRODUCTION  \nModern financial auditing practices generate substantial volumes of textual documentation, including audit work papers, client correspondence, management representations, and supporting evidence documents[1] . The comprehensive review of this documentation represents a fundamental requirement for audit quality and regulatory compliance, yet poses significant challenges in terms of resource allocation and consistency[2] . Professional auditing standards require thorough examination of audit evidence to support audit op","cbCaiotVIkKzv5DS","https://ap.wps.com/l/cbCaiotVIkKzv5DS","pdf",785669,1,9,"English","en",105,"# Introduction\n## Challenges in audit documentation review\n## Limitations of traditional keyword/manual methods\n## Motivation for machine learning and NLP\n# Proposed machine learning-enhanced framework\n## NLP and supervised learning workflow\n## Risk indicators, compliance issues, and anomaly detection\n# Experimental validation and results\n## Classification accuracy and time reduction\n## Identification of high-risk documentation\n# Implications for audit quality and regulatory compliance","[{\"question\":\"What problem does the study address in audit documentation review?\",\"answer\":\"The study targets the time-intensive and inconsistent nature of reviewing large volumes of textual audit evidence using manual and keyword-based methods.\"},{\"question\":\"What does the proposed framework automate?\",\"answer\":\"It uses NLP with supervised learning to classify, prioritize, and extract relevant information from audit documentation, including risk indicators, compliance issues, and anomalous patterns.\"},{\"question\":\"What results does the study report from validation on real datasets?\",\"answer\":\"The framework achieves 91.4% accuracy in document classification and reduces manual review time by 68%, while improving efficiency, consistency, and coverage.\"}]","MACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW - 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