Foundations and Architectures of Artificial Intelligence for Motor Insurance
This work addresses the need for reliable, production-grade AI systems in high-stakes motor insurance, though it appears incremental as it builds on existing transformer methods and MLOps practices.
The handbook tackles the problem of automating motor insurance processes like risk assessment and claims processing by developing a vertically integrated AI paradigm with domain-adapted transformer architectures, resulting in a scalable pipeline deployed in nationwide systems in Thailand.
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.