Application of Machine Learning in Taxation: A Systematic Literature Review

Information and Communication Technology (ICT) has significantly impacted tax administration worldwide. Various recent studies underscore the efficacy of machine learning and big data mining methodologies in tackling numerous issues, notably in the domain of financial fraud detection, inclusive of tax fraud. Beyond tax fraud, machine learning applications extend to the detection of tax avoidance and compliance. Despite the extensive research conducted on machine learning's application in taxation, systematic studies remain relatively scarce. Therefore, this study aims to deepen the understanding of machine learning in taxation. It surveys the most recent methods how taxation applying machine learning. It reviews 29 relevant studies, which encompass various contexts, research focuses, and techniques or algorithms employed. Future studies should explore more in the context, focus, and technique used. © 2024 IEEE.