Machine Learning for Triple-Entry Accounting: Enhancing Transparency and Oversight

This study develops a conceptual framework for integrating Triple-Entry (TE) accounting with machine learning (ML) to enhance transparency in financial reporting and auditing. TE extends the double-entry system by introducing a cryptographic third entry that captures contextual metadata and strengthens auditability. Existing research has discussed TE models and blockchain implementations, yet there is limited exploration of how advanced analytics can operationalise these systems in practice. This paper reviews prior contributions, highlights the limitations of current approaches, and positions ML as a mechanism for anomaly detection, fraud prevention, and continuous oversight. The methodology is qualitative and analytical, based on a structured review of the accounting, blockchain, and ML literature, with a critical comparison of TE and multiparty computation (MPC) approaches. A workflow for transforming TE data into ML-ready features is outlined, linking technical methods to objectives such as compliance monitoring and forecasting. The proposed framework advances theoretical understanding while also identifying practical applications, including regulatory reporting and privacy-preserving audits. Contributions include the articulation of a research agenda for empirical testing of ML-enabled TE systems and guidance for auditors, regulators, and system designers on embedding transparency in distributed financial environments.