Extract JSON from Bank Statements in Node.js Without Prompt Engineering

If you're building a fintech app, an expense tracker, or a lending platform, you eventually hit the hardest problem in software engineering: parsing unstructured PDFs.

Bank statements are notoriously difficult to parse. Every bank has a different layout. Some have tables that span multiple pages. Some embed images. If you try to use generic OCR or basic regular expressions, you will quickly find yourself managing thousands of lines of fragile parsing logic.

The Problem with Generic LLMs

You might think, "I'll just throw the PDF text into ChatGPT or Claude and ask for JSON." While this works for a quick prototype, scaling it in production is a nightmare. You will encounter:

• Hallucinations: The LLM might invent transactions or misread decimal points.
• Formatting Errors: It might return markdown code blocks instead of raw JSON, breaking your JSON.parse().
• Context Limits: Long 50-page statements will crash the prompt.

The Solution: The PDF Pro API

We built PDF Pro to solve this exact problem. We spent hundreds of hours fine-tuning our extraction prompts and testing them against thousands of real-world bank statements from HDFC, SBI, Chase, and BoA.

Instead of doing the hard work yourself, you can just hit our API.

Step 1: Extract the Text Locally

First, extract the raw text from the PDF on your end (using pdf-parse or pdfjs-dist in Node.js). This ensures you only send raw text, reducing bandwidth.

Step 2: Hit the API

Make a simple POST request to our analysis endpoint:

const response = await fetch("https://www.pdfpro.co.in/api/analyze-bank-statement", {
  method: "POST",
  headers: {
    "Authorization": "Bearer sk_live_YOUR_KEY",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    text: extractedPdfText
  })
});

const financialData = await response.json();
console.log(financialData.hiddenSubscriptions);

What You Get Back

Our API guarantees a perfectly formatted JSON response containing:

• A Financial Health Score (0-100)
• Total estimated income and expenses
• A list of Hidden Subscriptions (e.g., Netflix, AWS, Gym memberships)
• Top spending categories

You can drop this directly into your database or UI. No parsing required.