Bedrock Data Automation Learns to Extract Data from Tax Forms and Statements

Amazon Bedrock Data Automation from AWS has learned to accurately extract information from financial documents. The system automatically processes bank statements, tax forms, contracts, and other documents — a task that typically requires hours of manual work.

Which documents does the system process?

Amazon trained Bedrock Data Automation on examples of four common types of financial documents. Bank statements contain information about thousands of transactions, each of which must be correctly recognized. Tax forms W-2 (employee income statements) and 1099-B (investment income reports) require accurate extraction of numerous details. Supplier contracts are often formatted individually, creating additional challenges for algorithms.

Why is this more complex than simple OCR?

Ordinary optical character recognition (OCR) cannot handle financial documents. The system must not only "read" the text but understand its structure and context. For example, in a statement table, you need to correctly link the amount with the date and transaction description. In tax forms, numbers are often located in specific places, and their value depends on the surrounding content.

Amazon Bedrock uses language models for deep understanding of document content. The system learns from examples: it sees the original document and its correctly filled digital version, then generalizes patterns for new cases.

How does the system work in practice?

The automation process consists of several stages:

• Document type recognition — the system determines whether it's a statement, tax form, or contract
• Key field localization — the algorithm searches for where the needed data is located
• Value extraction — the system converts found text into a structured format
• Confidence assessment — the model indicates the probability of error for each field
• Validation — if necessary, the document is sent for manual review

For most documents, the process is fully automatic. When confidence is low, the result goes through a human.

Business savings

Fintech companies can speed up processing of applications requiring financial documents. Instead of 30 minutes of manual work per application, the system will handle it in minutes. Accounting departments can automate data entry from receipts and reports into the accounting system. Banks can verify documents faster when issuing loans.

What does this mean?

The financial industry is gradually transitioning to AI solutions for routine document work. This is not a replacement for people, but an expansion of their capabilities — an employee can review results in a minute instead of an hour of manual work.