Development of An Intelligent NLP-Based Audit Plan Knowledge Discovery System

Abstract

High level of knowledge and expertise are required in auditing, which makes it a knowledge-intensive professional service. Auditors' brainstorming meetings involve various topics on risks, which provide numerous valuable knowledge on how auditors identify and assess risks and achieve decisions. However, it is very difficult to retrieve useful knowledge from these meeting conversations. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that will continuously and automatically extract important knowledge from audit brainstorming discussions and provide decision support to auditors in future engagement cases.