  The emergence of Large Multimodal Models (LMMs) marks a significant milestone
in the development of artificial intelligence. Insurance, as a vast and complex
discipline, involves a wide variety of data forms in its operational processes,
including text, images, and videos, thereby giving rise to diverse multimodal
tasks. Despite this, there has been limited systematic exploration of
multimodal tasks specific to insurance, nor a thorough investigation into how
LMMs can address these challenges. In this paper, we explore GPT-4V's
capabilities in the insurance domain. We categorize multimodal tasks by
focusing primarily on visual aspects based on types of insurance (e.g., auto,
household/commercial property, health, and agricultural insurance) and
insurance stages (e.g., risk assessment, risk monitoring, and claims
processing). Our experiment reveals that GPT-4V exhibits remarkable abilities
in insurance-related tasks, demonstrating not only a robust understanding of
multimodal content in the insurance domain but also a comprehensive knowledge
of insurance scenarios. However, there are notable shortcomings: GPT-4V
struggles with detailed risk rating and loss assessment, suffers from
hallucination in image understanding, and shows variable support for different
languages. Through this work, we aim to bridge the insurance domain with
cutting-edge LMM technology, facilitate interdisciplinary exchange and
development, and provide a foundation for the continued advancement and
evolution of future research endeavors.
