Bank Reconciliation Automation is the specific subset of reconciliation focused on bridging the gap between modern tech stacks and legacy banking protocols. While fintechs operate on REST/GraphQL APIs and JSON, the global banking infrastructure still relies heavily on flat files and decades-old standards like SWIFT MT940, BAI2, and CAMt.053.
For a CTO, the challenge is not just "matching numbers," but building a translation layer that can ingest these archaic formats, parse their distinct codes, and map them to a modern database schema without manual intervention.
The "Rosetta Stone" Problem: Parsing Formats
Banks rarely adhere strictly to file standards. A standard MT940 file structure includes "Tag 61" for transaction details and "Tag 86" for information to the account owner. However, Bank A might put the unique Reference ID in Tag 61, subfield 7, while Bank B puts it in Tag 86, line 2. Automation requires a flexible parsing engine that defines specific "Bank Profiles." BAI2: A US-centric format. It uses numeric type codes (e.g., 115 for Lockbox Deposit). The parser must map 115 to the system's internal deposit enum. MT940/942: SWIFT standards. The parser must use Regex to extract structured data (like IBANs or Reference IDs) from unstructured text blocks within the file.
Automating the "Cash Application"
Once parsed, the data represents the "Bank Side" of the equation. Automation links this to the "Ledger Side." This is critical for "Cash Application"—the process of applying an incoming payment to an open invoice. By automating the parsing of the Remittance Information field in the bank file, the system can extract invoice numbers. This allows the system to automatically close open invoices in the ERP, freeing up credit limits for customers without waiting for a finance manager to review the bank statement.
