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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.

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How Existing Systems Work

  1. OCR Extraction - Converts scanned documents into raw text.
  2. Template Matching - Maps structured data based on known layouts.
  3. 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.

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Where These Methods Fail

ApproachStrengthsLimitations
OCR ExtractionWorks well on clean PDFsStruggles with inconsistent formats and handwritten notes.
Template MatchingFast for structured documentsFails when bank layouts change or statements contain multiple pages.
Rule-Based ParsingAutomates transaction classificationBreaks with unknown vendors, typos, and non-standard cash flows.

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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.