How to Extract Financial Data from PDF Financial Statements (Step-by-Step Guide)

Igor Strelkov

Published on July 1, 2025

How to Extract Financial Data from PDF Financial Statements (Step-by-Step Guide)

Extracting financial data from PDF statements can be time-consuming and error-prone, especially when done manually. Whether you're an analyst, accountant, or founder building your own financial model, automating this process can save hours and improve accuracy. This guide walks you through the step-by-step process of extracting financial data from PDFs and converting them into standardized financial statements.

Step 1: Understand the Structure of the Financial Statement

Before extraction, it’s important to understand what you’re working with. A typical financial statement contains:

Income Statement (Profit & Loss)
Balance Sheet
Cash Flow Statement
Notes to Financials

These may be embedded in:

Native PDFs (machine-readable text)
Scanned PDFs (image-based, requiring OCR)

Step 2: Choose the Right Tool for Extraction

Manual extraction isn’t scalable. Instead, use tools that automate the process:

Option A: Use Assess Finance

Assess Finance is a tool built to extract financial data from uploaded PDF statements and automatically generate standardized:

Income Statements
Balance Sheets
Cash Flow Reports

It works with both scanned and digital PDFs, applying OCR where needed, and ensures data is mapped correctly to financial formats.

Option B: Python Libraries (for technical users)

pdfplumber: Great for extracting text from PDFs.
PyMuPDF or pdfminer.six: Used for more granular PDF parsing.
Tesseract OCR: To convert scanned images into text.

Step 3: Upload or Parse the PDF

With Assess Finance:

1. Go to assess.finance

2. Upload your PDF statement

3. Let the tool process and extract the data

4. Download standardized financial statements in Excel or CSV

With Python:

1import pdfplumber
2
3with pdfplumber.open("financial_statement.pdf") as pdf:
4    for page in pdf.pages:
5        text = page.extract_text()
6        print(text)
7

Use data-cleaning tools like Pandas to reformat the extracted text into structured tables.

Step 4: Standardize the Output

The raw data may contain inconsistent headings, line items, and formats.

Group line items under standard categories (e.g., Operating Income, SG&A, Net Profit)
Use templates for Income Statement, Balance Sheet, and Cash Flow

Tools like Assess Finance do this automatically by mapping values to GAAP/IFRS-compatible structures.

Step 5: Validate and Clean the Data

Before using the output:

Cross-check totals (e.g., Assets = Liabilities + Equity)
Check year-end dates to match reporting periods
Verify OCR accuracy if from a scanned PDF

Step 6: Export and Analyze

Once your financials are structured:

Export to Excel, CSV, or feed into a BI tool
Perform ratio analysis, trend analysis, or financial modeling

Summary

Extracting financial data from PDF statements doesn’t have to be a painful manual task. With tools like Assess Finance, you can upload a PDF and receive clean, standardized financials in minutes. Whether you're preparing for credit analysis, due diligence, or financial modeling, this step-by-step process ensures accuracy, consistency, and speed.

Try Assess Finance today at assess.finance