Book Image

Intelligent Document Processing with AWS AI/ML

By : Sonali Sahu
Book Image

Intelligent Document Processing with AWS AI/ML

By: Sonali Sahu

Overview of this book

With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation.
Table of Contents (16 chapters)
1
Part 1: Accurate Extraction of Documents and Categorization
6
Part 2: Enrichment of Data and Post-Processing of Data
10
Part 3: Intelligent Document Processing in Industry Use Cases

Understanding insurance claims processing document enrichment and review and verification

Let’s now check how the review and verification stage for our insurance claims processing use case works. We mentioned the review and verification steps in detail in Chapter 6, Review and Verification of Intelligent Document Processing. If you have not gone through that chapter, I recommend going through it first. In Chapter 6, Review and Verification of Intelligent Document Processing, we described high-level types of document verification steps. For example, we have a completeness check and an accuracy check to process a document in the review and validation stage of the IDP pipeline. During this stage, the user defines business rules to be checked against the extracted elements from documents. Let’s now see an example implementation to check for completeness of the document.

If you have not already executed step 1 to step 6 of the Understanding insurance claims processing extraction...