Artificial Intelligence and Blockchain for Rural Accounting Assurance: A Systematic Review and Conceptual Framework for Rural Entrepreneurship

Rural accounting systems operate under persistent constraints - fragmented records, heterogeneous data sources, and intermittent connectivity - that undermine the reliability of both financial reporting and sustainability-related disclosures. This study addresses these limitations by designing and internally validating the AI-Blockchain Accounting Framework for Rural Entrepreneurship (ABAF-RE), a modular architecture that integrates distributed-ledger mechanisms, explainable machine-learning analytics, and rule-based verification logic for ESG-oriented controls. The research combines a PRISMA-guided systematic review (2019-2025) with a structured conceptual modeling procedure to extract recurrent design requirements for verifiable provenance, offline-first evidence capture, deferred notarization, oracle-aware data ingestion, and rule-as-code validation under low-infrastructure conditions. Across the reviewed literature, hybrid artificial intelligence/machine-learning and deep-learning approaches are consistently discussed as enabling stronger auditability and evidence continuity through tamper-evident records, traceable data lineage, and programmable control enforcement; however, reported effects are typically task- and context-specific, with heterogeneous baselines and evaluation protocols that limit cross-study comparability and discourage the use of universal performance percentages. Building on these convergent mechanisms, ABAF-RE consolidates an asynchronously synchronized, offline-first blueprint intended to preserve auditable information flows, semantic coherence across sources, and multi-actor traceability in rural value chains. The study argues that distributed computational trust - grounded in cryptographic lineage, explainability-oriented governance, and programmable assurance - provides a defensible pathway to strengthen the quality of financial and ESG disclosures and to support continuous auditing workflows under real-world connectivity constraints, while framing quantitative impacts as hypotheses to be tested through pilots with pre-registered metrics and explicit baselines.