Open Access

Blockchain-Enabled PO/Invoice Reconciliation: Automating Audit Trails for Public Infrastructure Grants

4 Project Manager- OSP Financial Controls & Forecasting

Abstract

Abstract

Traditional Purchase Order (PO)-to-invoice reconciliation processes in infrastructure finance are often fragmented, opaque, and vulnerable to error or manipulation, especially within public-sector grant-funded projects. Manual validation and spreadsheet-based tracking make it difficult to maintain transparency, traceability, and compliance across multiple 6stakeholders. This paper proposes a blockchain-enabled framework for automating audit trails in PO/invoice reconciliation, ensuring data integrity, accountability, and real-time verification of financial transactions.

The study explores how distributed ledger technology (DLT) can integrate with existing enterprise resource planning (ERP) systems to record procurement events—purchase orders, goods receipts, and invoices—on an immutable, time-stamped ledger. Smart contracts are introduced to automatically validate invoice–PO matches and flag anomalies in payment amounts, vendor identities, or project milestones. Using simulated public infrastructure grant data, the proposed framework compares blockchain-assisted reconciliation to traditional FP&A workflows on metrics such as accuracy, processing time, and audit readiness.

The results demonstrate that blockchain-based reconciliation significantly enhances financial transparency, reduces manual effort, and mitigates fraud and double billing. Furthermore, integration with business intelligence dashboards enables continuous monitoring of fund utilization across projects. This research contributes to the emerging domain of financial technology in infrastructure governance by showing how blockchain can transform reconciliation from a reactive accounting process into a proactive, automated compliance mechanism.

How to Cite

Korde, A. (2026). Blockchain-Enabled PO/Invoice Reconciliation: Automating Audit Trails for Public Infrastructure Grants. Frontiers in Emerging Artificial Intelligence and Machine Learning, 3(1), 09–32. https://doi.org/10.64917/feaiml/Volume03Issue01-02

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