Applications of Machine Learning in Accounting: Current Trends and Future Perspectives

Machine learning (ML) is increasingly used to detect fraud, predict distress, analyze disclosures, and support audit work. Prior reviews map applications or methods but rarely connect accounting problems to ML design, validation, and governance choices. We reviewed 273 peer-reviewed Web of Science Core Collection articles published from 1992 to 2025, combining bibliometric science mapping with an antecedents-decisions-outcomes (ADO) synthesis enriched by a theory-context-method (TCM) lens. The literature clusters around distress and bankruptcy prediction, reporting quality, and fraud, bridged by audit analytics and textual analysis. ML adoption is driven mainly by assurance pressure, unstructured information, and digital process data; however, many studies rely on accuracy-oriented metrics, nontemporal validation, and explainability that is reported rather than tested. We show how accounting conditions should discipline ML evaluation and propose a research agenda for cost-aware, temporally robust, and governable ML in accounting.