Amazon Bedrock Data Automation (BDA) enables financial institutions to automate the extraction, validation, and analysis of data from various financial documents. These documents include tax forms, loan statements, and purchase orders, which often have unique formats and structures, making automation challenging with traditional OCR software. BDA uses foundation models to understand document context, recognize relationships between sections, and extract structured data.
Unlike models like Anthropic Claude, BDA offers custom extractions with industry-leading accuracy at a lower cost, along with features such as confidence scores for explainability and built-in hallucination mitigation. This post explores how BDA can accurately extract information from four common financial documents: bank statements, W-2 forms, 1099-B tax forms, and vendor contracts. The solution overview explains that BDA allows users to configure output using blueprints, which are templates defining data extraction and validation rules.
Blueprints can be either catalog or custom, with custom ones allowing organizations to create extraction patterns for specific needs. In this post, custom blueprints were created and validated using the BDA console.
Source: awsml
