构建面向车险的AI架构，实现从定损到理赔的全流程自动化。
Foundations and Architectures of Artificial Intelligence for Motor Insurance
- 采用领域自适应的Transformer架构，融合视觉与多模态信息进行车辆损伤分析。
- 在泰国全国车险系统中验证，实现端到端的理赔评估与承保自动化。
- 强调算法与MLOps协同演进，适合高风险工业场景的AI落地应用。
本手册系统阐述了面向车险的人工智能基础与架构，基于大规模真实部署实践。提出一种垂直整合的AI范式，将感知、多模态推理与生产基础设施统一为一个连贯智能栈，用于汽车风险评估与理赔处理。核心包括针对结构化视觉理解的领域自适应Transformer架构、关系型车辆表征学习及多模态文档智能，实现车辆损伤分析、理赔评估与承保流程的端到端自动化。各组件组成可在泰国全国车险系统实际约束下扩展的流水线。除模型设计外，手册还强调学习算法与MLOps实践的协同演化，建立将现代AI转化为高可靠性生产级系统的原理性框架。
原文摘要 · Abstract (English)
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.
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