Machine learning in management accounting research: Literature review and pathways for the future

Abstract

This paper explores the possibilities of machine learning (ML) methods in management accounting research and showcases one future avenue in practice by applying ML-based textual literature review to ML/AI research in accounting. The review reveals that machine learning methods in management accounting (MA) are still in their infancy, and current research in accounting has progressed in and focused mainly on three areas related to ML and AI: 1) effects on the field of accounting and the development of the accounting profession, 2) textual analysis related to accounting data/reports, and 3) prediction methods. Based on our literature review and recently published related ML research from other fields (e.g., finance and economics), we suggest that the most promising areas for MA research to employ ML-methods are: exploitation of the rich potential of various textual data sources; quantifying qualitative and unstructured data (for example, text and images) to create new measures; creating better estimates and predictions; and using explainable AI to interpret ML models in detail.