Insurers in the US collectively process millions of documents annually across most of their business processes, from distribution and underwriting to finance and claims. Manual document based processes are still in place, and for some insurance market segments, there is still a lot of manual data entry into core business systems.
There are now technology solutions that insurers can leverage to scan electronic documents and gather text for automating the process of updating their core business systems. These solutions can help solve two types of problems: extracting text from digital documents that are cleanly indexed (structured data) and extracting unstructured text from digital documents. The latter has traditionally been a bigger challenge for insurers, but there has recently been an increase in the number of software vendors offering technology solutions that address the challenge of transforming unstructured text from documents into structured data sets for automated processing.
Unstructured text-based documents are currently a focus for insurers who receive applications, medical notes, invoices, and other documents that can be processed by intelligent text ingestion, (ITI) software solutions supported with artificial intelligence (AI) and machine learning (ML), technology. These solutions are broader than optical character recognition (OCR) and intelligent character recognition (ICR) technologies, which focus on identifying characters in unstructured text. ITI solution providers have created technologies to help insurers and other companies digitally ingest these types of documents and allow the insurers to take action on the incoming data.
This report provides profiles of several vertical and horizontal industry solution providers that offer technology solutions for unstructured text extraction, classification, and related tools.
Read the full report on Novarica's website.