The Limitations of Traditional Reconciliation Systems

In legacy enterprise tools like SAP or Oracle, reconciliation typically follows a rigid, labor-intensive path:

• Data Extraction and Analysis: Teams manually dive into structured or semi-structured sources (e.g., SQL tables, CRM exports, Excel files, or PDFs) to pull relevant values. If data is unstructured — like scanned invoices or handwritten notes in images — it’s often ignored or requires separate tools and human effort to digitize.

• Data Matching: Humans spend hours hunting for corresponding fields across documents. Matching an invoice number from a PDF to a bank transaction in an Excel sheet might involve sifting through variable formats, typos, or inconsistent naming conventions.

• Finding Discrepancies: Once matched, comparisons are done via predefined rules, but edge cases (e.g., slight description variances or swapped fields) lead to a flood of false positives or overlooked issues.

• Resolving Discrepancies: Exceptions pile up, demanding manual review and correction, often in a separate system like a CRM or spreadsheet.

These systems depend on clean, structured data and deterministic rules, which break down in real-world scenarios. For instance, SAP’s matching modules work well for uniform datasets but require custom scripting or human tweaks for variability, leading to skyrocketing exceptions and wasted time.