Financial Report Redaction: Methods for Automatically Identifying and Hiding Bank Card Numbers and ID Numbers Offline Office File Protection

Hidden Characteristics of Sensitive Information in Financial Reports and Identification Challenges

• Format fragmentation: Bank card numbers may be split with spaces (“6228 4800 1234 5678”), hyphens (“6228-4800-1234-5678”), or stored across cells (e.g., “622848” in column A and “0012345678” in column B). ID numbers might be partially obscured (“310********1234”) or nested in text (“Born on January 1, 1980 (ID: 310XXXXXXXX1234)”).
• Diverse carriers: Beyond Excel formula cells and Word tables, sensitive information may hide in scanned images (e.g., handwritten reimbursement forms), PDF annotations, or even data labels in charts.
• Business interference: Financial reports contain numerous numbers resembling sensitive data (e.g., invoice numbers, contract codes, amounts). Traditional keyword matching often misidentifies these (e.g., mistaking an 18-digit contract number for an ID number).

Core Technologies and Implementation Paths for Automatic Sensitive Information Identification

1. Multi-Dimensional Rule Engine: Targeting Structured Sensitive Data

• ID number verification: Combines the 18-digit encoding rules (6-digit administrative region code + 8-digit birth date + 3-digit sequence code + 1-digit check code) and uses a checksum algorithm (weighted sum of the first 17 digits modulo 11) to eliminate invalid matches. For example, it automatically excludes “11010119000101123” (insufficient length) or “110101202302301234” (invalid date).
• Bank card number parsing: Follows ISO/IEC 7812 standards to identify 13–19 digit card numbers (including prefixes for UnionPay, VISA, MasterCard, etc.). It can automatically splice numbers split by symbols (e.g., merging “6228 4800 1234 5678” into a complete card number) and verify validity via the Luhn algorithm.
• Format adaptability: For scenarios like cross-cell splitting or hidden row/column storage in financial reports, the system analyzes 关联性 between adjacent cells to identify “split-stored card numbers” (e.g., A1=622848 and A2=0012345678 → merged into 6228480012345678).

2. AI Semantic Analysis: Mining Unstructured and Hidden Information

• OCR + structured restoration: For scanned reimbursement forms, handwritten bank receipts, and other images, OCR first recognizes text (supporting Chinese, English, and handwritten fonts), which is then converted into structured data for sensitive information extraction. A manufacturing company used this feature to successfully identify employee ID numbers from over 500 handwritten travel invoices with 99.2% accuracy.
• Contextual semantic understanding: Analyzes text context to exclude interference. For example, it identifies the card number in “Supplier account: 6228480012345678” while ignoring “Order number: 2023062812345678” (16 digits but semantically irrelevant) in the same table.
• Cross-carrier associated retrieval: Links and analyzes attachments in reports (e.g., PDFs embedded in Excel, images in Word) to ensure sensitive information hidden in subsidiary files is not missed.

3. Batch Processing and Visual Verification

• Full automated scanning: Supports batch upload of Excel, Word, PDF, and other formats (processing 1,000+ reports at once). It completes sensitive information identification for 10,000+ pages in 10 minutes, 300 times more efficient than manual work.
• Visual marking: Highlights sensitive information positions in the original file (e.g., “ID number: 310101190001011234” → redacts the numeric part), facilitating secondary verification by financial staff and reducing misjudgments.

Compliant Redaction: Balancing Security and Data Usability

• Partial masking: Retains the first 6 and last 4 digits for key identification, replacing the middle part with “*” (e.g., ID number “310101190001011234” → “3101011234″; bank card number “6228480012345678” → “62284878″). This complies with the “minimum necessity” principle in the Personal Information Protection Law while retaining data traceability (e.g., verifying the last 4 digits of a card number).
• Encrypted storage: For information that needs to be fully retained but with restricted access (e.g., account details in CFO approval forms), national encryption standard SM4 algorithm is used. Only authorized users can view the original text after entering a key; others see only ciphertext.
• Field-level deletion: For redundant sensitive information in archived reports (e.g., ID numbers in historical payroll sheets), one-click field deletion is supported to completely eliminate leakage risks.

Why Choose bestCoffer for Financial Report Redaction?

1. Built-in industry rules: Presets a dedicated identification library for financial fields (e.g., bank account encoding rules, provident fund account formats), avoiding misjudgments of financial terminology by general tools.
2. Strong format compatibility: Perfectly parses complex Excel formulas, Word revision modes, PDF dynamic forms, and other financial formats, ensuring nested information is not missed.
3. Compliance traceability: Redaction operations are fully logged (recording redaction time, operator, and rule version), meeting compliance audit requirements such as SOX and Level 2 Cybersecurity Protection.