Turning a messy PDF into clear, explainable signals a lender can trust.

A lender looking at a bank statement isn't really interested in the document. They want answers.

Does this person earn regularly?

Is this statement genuine?

What do they already owe every month?

Getting from a PDF to those answers is a surprisingly interesting engineering problem. This article walks through the main steps, with small Python examples you can adapt.

Why extracting bank statement data is harder than it looks

Imagine trying to read the menu at 50 different restaurants, where every menu puts the prices, dishes and sections in a different place. That's bank statements.

Each bank uses its own layout. Columns move around. Some statements combine debit and credit into one column with a sign, others split them. Narrations are cryptic, like NEFT/CR/ACME PVT LTD/SAL OCT . Tables continue across pages, sometimes repeating headers halfway through.

And that's the clean case.…