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Bank Statement Analysis: A Complete Guide

Nanonets

They also lack robust security features, potentially exposing sensitive financial data. Version control can be challenging, particularly in collaborative environments, and spreadsheets offer limited audit trails for tracking changes. This analysis also facilitates audits and financial investigations when necessary.

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How to extract data from payslips using OCR?

Nanonets

Net pay : In-hand amount after all deductions Year-to-date (YTD) totals: Total earnings and deductions for the current year Convert payslips OCR can convert payslips into PDF, TXT/Doc, CSV, XLSX, XML, or JSON formats. Data security With a surge in free OCR tools, data security is at a major risk.

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How to Use AI in Bank Statement Processing

Nanonets

The extracted data is then sent for bank statement analysis , further processing, and accounting.    Bank statement processing is essential for accurate reconciliation , auditing, and financial reporting. Maintain an efficient audit trail for future retrieval. Ensure all statements are properly scanned and readable.

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What is PO Matching? And how to automate it?

Nanonets

MS-Word documents), data entry files (e.g., MS-Excel files), structured XML documents from Electronic Data Interchange (EDI), PDFs and image files, and sometimes as hard copy documents. Audit Readiness: POs, GRNs, and invoices are among the most common documents asked during audits.

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Automated order processing: Boost efficiency

Nanonets

Step 4: Export your data Once the system has extracted and validated the data from your orders, you can export the data to your preferred destination. Nanonets supports multiple output formats, including JSON, XML, CSV, and direct API calls to other systems. Implement robust encryption for data at rest and in transit.