Accuracy challenges in financial data processing
Corporate finance departments often face problems such as incorrectly entered invoice information (average error rate of 3.7%) and duplicated voucher numbers, making month-end reconciliation difficult.
Three-phase upgrading program
1. Pre-processing phase
- Set validation rules in Cloudsquid (e.g. "Amount must be positive")
- Enable automatic de-duplication (based on invoice number/date combination)
2. Extraction phase
- Matching using templates: pre-saved field mapping for fixed-format tickets (e.g. VAT invoices)
- Double-checking enabled for amount fields: recognizes both numeric and uppercase text
3. Post-processing phase
- Flag outliers (e.g., out of 3σ range) using built-in calibration tool before exporting
- Configure mandatory type checking of fields (numeric/text/date) when connecting directly to ERP via API
Effectiveness Verification
A manufacturing customer after implementation:
- Accounts payable processing error down 98%
- Reduction in month-end closing time 60%
This answer comes from the articleCloudsquid: upload documents and describe requirements for intelligent extraction of structured dataThe
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