Year: 2026
Type: article
Abstract: Large insurance claims handling is a complex, knowledge-intensive process that requires the analysis of heterogeneous information sources and the reuse of past experience distributed across multiple organizational data sources. Consequently, a significant portion of decision-making knowledge is embedded in historical claims records and internal documents, making systematic access and reuse challenging. This paper presents Enterprise Knowledge Assistant (EKA), a collaborative multi-agent AI system designed to act as a sparring partner for large claims handlers. EKA integrates claims structured and unstructured data with an archive of more than five thousand historical cases related to claims management, enabling retrieval, interpretation, and synthesis of relevant past cases and decision patterns. The system is organized as a set of specialized AI agents, each responsible for distinct tasks including claim context analysis, knowledge extraction, document synthesis, and interaction with human users. Through agent collaboration, EKA provides decision support by analyzing comparable historical cases, uncovering hidden correlations, and extracting insurance wisdom, while keeping the human expert firmly in control. The paper describes the system architecture and reports an industrial case study evaluating EKA in a real insurance environment. Results indicate improved knowledge reuse and reduced analysis effort in large claims handling.
Source: Applied System Innovation
DOI: 10.3390/asi9030062
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W7137924544