Machine learning approach to identify performance audit topics for different government sectors

A government performance audit is an independent evaluation of a government entity's activities and operations aimed at improving its efficiency, effectiveness, and accountability. Audit offices are frequently facing the challenge of selecting an audit topic for different government sectors that justifies the use of public money to conduct the performance audit. Text mining techniques have been rarely mentioned in association with selecting performance audit topics in the literature. In this work, we identify potential performance audit topics using topic modelling, an unsupervised machine learning approach. Topic modelling has been employed to create a demonstration system aimed at showcasing the utility of text mining tools in identifying potential audit topics. The outcome of this study suggests that incorporating text mining in the stage of identifying performance audit topics will streamline the topic selection process and decrease the amount of time required for manual information gathering at the outset.