ARAG is a five-billion-EUR insurance group spanning 19 countries, with legal insurance as its flagship business. The tech stack reveals active migration toward cloud-native architecture: SAP ecosystem (S/4HANA, BTP, Fiori) paired with Azure data platform (Data Lake, Data Factory, Synapse), Python/FastAPI for modern services, and Docker/Spring Boot adoption signal engineering is building alongside legacy modernization. Active projects target workflow automation and AI agents, while hiring velocity is accelerating—but sales roles (106 of 158) vastly outnumber engineering (11), pointing to a sales-led growth strategy under organizational strain.
Notable leadership hires: Customer Care Lead, Head of Sales
ARAG is Europe's largest family-owned insurance enterprise, headquartered in Düsseldorf. The group operates across legal, composite, health, and pension insurance, selling to individuals and businesses across 19 countries including the US, Canada, and Australia. With over 5,000 employees generating €2.37 billion in annual premium income, ARAG holds market leadership in legal insurance globally and operates through direct branches, subsidiaries, and shareholdings in major international markets. Current focus areas include digital transformation of core operations, automation of internal workflows, and partner ecosystem expansion.
ARAG uses SAP (S/4HANA, BTP, Fiori, Workzone), Azure (Data Lake, Data Factory, Synapse), Python, FastAPI, React, Power BI, Databricks, and Dynatrace. Currently adopting Spring Boot and Docker for modernization.
ARAG is headquartered in Düsseldorf, Germany, and operates as a privately held company with 5,001–10,000 employees across 19 countries.
ARAG's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.