Transaction matching is the algorithmic core of reconciliation. It is the logic that determines if Record A from the internal ledger is the same entity as Record B from the bank statement. In simple systems, this is a distinct WHERE clause (e.g., WHERE amount = X AND id = Y). In complex fintech infrastructure, matching is a multi-layered heuristic process.
Effective matching logic must handle data anomalies, time drift, and aggregation differences (bundling) without yielding false positives, which are dangerous in financial accounting.
Matching Topologies (1:1, 1:N, N:M)
Automation must account for how money moves versus how it is recorded. One-to-One (1:1): The ideal state. One internal ledger entry ($50) matches one bank transaction ($50). One-to-Many (1:N): A single bank deposit ($1,000) represents ten individual customer payments of $100 recorded in the ledger. This is common with "Batch Settlements" from card networks. The algorithm must query the ledger for a sum of pending transactions that equals the bank line item. Many-to-Many (N:M): The most complex scenario. Multiple partial payments cover multiple invoices, settled in random bundles. This usually requires a "reference-based" matching approach (matching specific invoice IDs found in the metadata) rather than purely amount-based logic.
Tolerance Windows and Thresholds
Strict equality checks often fail in the real world. Automated systems use configurable tolerances: Time Tolerance: A payment initiated on Friday might settle on Tuesday. The matcher looks for the transaction within a Date +/- 3 Days window. Amount Tolerance: Due to FX fluctuations or cross-border fees, a $100.00 invoice might arrive as $99.85. The system defines a "tolerance threshold" (e.g., < $1.00). If the variance is within the threshold, the match is accepted, and the difference is automatically booked to a "Variance/Expense" account. If it exceeds the threshold, it breaks for manual review.
