Most mid-sized and large insurance companies process policy data valued at nearly USD 1 billion annually. This involves high document volumes during the insurance life cycle from quotation to final claim. The IT environment to support this typically involves document management platforms that are tightly integrated with core platforms to facilitate policy administration, billing, and claims.
These document solutions are essential to generate large policies, claims, and billing dockets. However, the manual process of policy document verification is onerous given the numerous documents involved. This adversely influences policy rates, complexity, and the verbiage used. The unique challenges faced by insurance companies are:
A cognitive automation approach using SLICE (Self-Learning Intelligent Content Extractor) specifically leverages AI/ML-based content extraction methodology applied to diverse documents including policy dockets, images, signatures, and others. The information extracted from these key insurance documents is then manually verified for legal, operational, and regulatory correctness. The SLICE-based solution optimizes the product quality and streamlines the release process resulting in overall improvement of operational efficiency.
Foundational Steps for Content Extraction and Storage