A standards-based ontology and support for Big Data Analytics in the insurance industry

Abstract

Standardization efforts have led to the emergence of conceptual models in the insurance industry. Simultaneously, the proliferation of digital information poses new challenges for the efficient management and analysis of available data. Based on the property and casualty data model, we propose an OWL ontology to represent insurance processes and to map large data volumes collected in traditional data stores. By the virtue of reasoning, we demonstrate a set of semantic queries using the ontology vocabulary that can simplify analytics and deduce implicit facts from these data. We compare this mapping approach to data in native RDF format, as in a triple store. As proof-of-concept, we use a large anonymized dataset for car policies from an actual insurance company