Merchant Cash Accounting
Current Approaches
Why traditional OCR and rule-based systems struggle with financial data extraction.
Industry Standard Approaches
Most financial document processing systems rely on OCR (Optical Character Recognition), template matching, and rule-based parsing. While effective for structured data, they fail in real-world financial scenarios due to inconsistent layouts, multi-line transactions, and contextual dependencies.
How Existing Systems Work
- OCR Extraction - Converts scanned documents into raw text.
- Template Matching - Maps structured data based on known layouts.
- Rule-Based Parsing - Uses predefined rules to categorize transactions.
While these methods seem effective, they fall short when dealing with unstructured financial documents like bank statements.
Where These Methods Fail
| Approach | Strengths | Limitations |
|---|---|---|
| OCR Extraction | Works well on clean PDFs | Struggles with inconsistent formats and handwritten notes. |
| Template Matching | Fast for structured documents | Fails when bank layouts change or statements contain multiple pages. |
| Rule-Based Parsing | Automates transaction classification | Breaks with unknown vendors, typos, and non-standard cash flows. |
Why OCR & Rule-Based Parsing Don’t Work for MCA
Bank Statements Are Not Standardized
- Every bank uses different layouts, fonts, and column structures.
- Traditional parsing fails when banks introduce new formats.
- OCR cannot differentiate between metadata and actual transactions.
Financial Data Requires Context
- A deposit from “Acme Corp.” could be revenue, a loan, or a refund.
- OCR cannot determine transaction intent without additional context.
- Rule-based methods struggle with complex cash flow patterns.
Multi-Line & Mixed Data Transactions
- Many bank statements split transactions across multiple lines.
- OCR cannot reconstruct fragmented transactions accurately.
- MCA repayment detection requires linking transactions across months.