Identifying and categorizing adverse remarks from audit reports for knowledge base creation and generating recommendations

Abstract

Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, present disclosure provides system and method that address the task of generating audit recommendations that can help auditors to focus their investigations. Adverse remarks, financial variables mentioned in each sentence are extracted/identified from audit reports and category tag is assigned accordingly, thus creating a knowledge base for generating audit recommendations using a trained sentence classifier. In absence of labeled data, the system applies linguistic rule(s) to identify adverse remark sentences, and automatically create labeled training data for training the sentence classifier. For a given financial statement and financial variables in the audit report that contribute to suspiciousness, the system compares these with the extracted knowledge base and identify aligned adverse remarks that help auditor(s) in focusing on specific directions for further investigations.