FIN FORMAT GPT
Author :CA. INDERJEET KAUR BAMRAH
Author :CA. INDERJEET KAUR BAMRAH
Introduction:
With the help of advanced AI tools like ChatGPT, it is now possible to seamlessly convert and structure financial data across multiple formats such as PDFs, scanned documents, Excel files, and Word documents. Traditional methods of format conversion—especially for scanned or image-based financial documents like Balance Sheets, Profit & Loss Accounts, and Cash Flow Statements—are often time-consuming, error-prone, and may require paid OCR tools.
FIN FORMAT GPT leverages AI-driven capabilities to automate this process. Whether it's extracting tabular data from an image-based PDF, converting unstructured text into clean Excel tables, or transforming Word documents into properly formatted financial reports, the tool ensures accuracy, speed, and consistency.
Not only does this drastically reduce manual effort, but it also improves data integrity and makes financial analysis, reporting, and compliance easier and more efficient for professionals like Chartered Accountants, auditors, and finance teams.
USE CASE -1. Bank statement Structuring (PDF to EXCEL) Prompt
Problem : A PDF containing a bank statement had both Debit and Credit entries appearing on the same line. The requirement was to restructure the data by separating Debit and Credit into different columns and calculating separate totals for each.
Purpose : This use case enables accurate conversion of bank statements from PDF to Excel by extracting all entries exactly as they appear, organizing Debit and Credit columns separately, and calculating their totals. It ensures a structured, error-free, and analysis-ready Excel output for financial review or reconciliation.
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Output:
USE CASE – 2. Convert PDF (IMAGE BASED) TO EXCEL
Problem Statement: The available PDF was a scanned, image-based document containing the Balance Sheet, Profit and Loss Account, and Cash Flow Statement. Converting this into an editable Excel format typically requires OCR (Optical Character Recognition) technology, which often incurs additional costs when using third-party platforms.
Purpose : This use case leverages OCR to convert scanned (image-based) financial statements into structured Excel tables, preserving the exact format and content. It ensures accurate replication of Balance Sheet data (Assets and Liabilities) for easy analysis and reporting.
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Output:
USE CASE -3. Comparison Sheet Excel and PDF
Problem statement :
An Ind AS-based Balance Sheet in Excel for 31.03.2024 and 31.03.2023 showed a mismatch between the Asset and Liability sides. To identify the source of this discrepancy, a line-by-line comparison with the previous year's Balance Sheet (prepared under old Accounting Standards and available only in PDF format) was required. Manually performing this task typically takes 45 minutes to an hour and carries a significant risk of missing information.
EXCEL SCREENSHOT:
AS BASED BALANCESHEET PDF
Purpose : This use case enables side-by-side comparison of financial data from an Excel sheet and a PDF version of the Balance Sheet across two years (2023 & 2024). It helps identify discrepancies line-by-line while maintaining the original format, with separate sheets for Assets and Liabilities in a structured Excel file.
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Output: