Automated Financial Data Integrity: An Algorithmic Approach to Robust Daily Bank Reconciliation

Financial data reconciliation remains a foundational requirement for ensuring the integrity, reliability, and auditability of enterprise financial operations. Yet, both ERP platforms and banking systems frequently output inconsistent and structurally flawed ledger exports in which row ordering, running balances, date grouping, and even intra-day sequencing are corrupted or lost. These defects disrupt automated workflows, break chronological consistency, and make conventional reconciliation tools—including native ERP matching utilities—unreliable. This research introduces a deterministic accounting algorithm, Balance Reconstruction via Transactional Delta Accumulation (BR-TDA), which reconstructs the true chronological ledger using cumulative credit–debit deltas. Because reconstruction is performed directly from transactional invariants rather than exported running balances, the approach becomes inherently order-invariant and resilient to scrambled records, malformed entries, descending-order bank statements, and partial data corruption. We formally define the reconstruction problem, provide a mathematical proof of correctness under non-ideal data conditions, and validate the method on diverse real-world datasets. Extensive empirical evaluation demonstrates that BR-TDA consistently identifies the earliest divergence between ERP internal ledgers and bank statements, even when ERP exports contain intra-day disorder or when banks list transactions strictly in reverse chronological order. The framework also employs a cumulative mismatch tracking heuristic to isolate discrepancy origins with 100% accuracy. Overall, the method outperforms existing heuristic reconciliation approaches, provides deterministic and audit-ready computational proofs, and significantly reduces manual workload for small- and medium-enterprise accounting environments. This work offers a novel and highly efficient foundation for next-generation automated financial reconciliation systems