Foundations and Architectures of Artificial Intelligence for Motor Insurance

TL;DR

This paper systematically explores AI architectures for motor insurance, integrating perception, reasoning, and production systems to automate vehicle damage analysis and claims processing in large-scale, real-world settings.

Contribution

It introduces a unified AI framework with domain-adapted transformer models for structured visual understanding and multimodal document analysis tailored for motor insurance applications.

Findings

Developed scalable AI pipeline for vehicle damage assessment

Achieved end-to-end automation of claims processing

Established principles for deploying reliable AI in industrial insurance environments

Abstract

This handbook presents a systematic treatment of the foundations and architectures of artificial intelligence for motor insurance, grounded in large-scale real-world deployment. It formalizes a vertically integrated AI paradigm that unifies perception, multimodal reasoning, and production infrastructure into a cohesive intelligence stack for automotive risk assessment and claims processing. At its core, the handbook develops domain-adapted transformer architectures for structured visual understanding, relational vehicle representation learning, and multimodal document intelligence, enabling end-to-end automation of vehicle damage analysis, claims evaluation, and underwriting workflows. These components are composed into a scalable pipeline operating under practical constraints observed in nationwide motor insurance systems in Thailand. Beyond model design, the handbook emphasizes the co-evolution of learning algorithms and MLOps practices, establishing a principled framework for translating modern artificial intelligence into reliable, production-grade systems in high-stakes industrial environments.