Foundations and Architectures of Artificial Intelligence for Motor Insurance
Abstract
This handbook presents a comprehensive and systematic framework for artificial intelligence in motor insurance, grounded in real-world production deployment. It documents the design, development, and operationalization of the MARSAIL (Motor AI Recognition Solution Artificial Intelligence Laboratory) ecosystem, one of Thailand’s earliest large-scale AI deployments for automotive insurance. The handbook integrates perception, multimodal reasoning, and production infrastructure into a cohesive intelligence stack for vehicle risk assessment, claims processing, and underwriting workflows. Core contributions include domain-adapted transformer architectures for structured visual understanding, relational vehicle representation, multimodal document intelligence, hierarchical attention refinement, and knowledge-enhanced model distillation. Beyond model innovation, the handbook emphasizes scalable pipelines, MLOps integration, and efficiency-focused engineering to ensure deployment-ready, production-grade AI systems. Over four years of iterative research and engineering, the handbook codifies a practical blueprint for translating cutting-edge AI research into robust, interpretable, and scalable solutions in high-stakes industrial environments.
Discover the definitive guide to AI in motor insurance with this comprehensive handbook, available at arXiv:2603.18508. Covering everything from domain-adapted transformer architectures to scalable, production-ready pipelines, it unifies perception, multimodal reasoning, and claims processing into a cohesive intelligence stack. Whether you’re building models for vehicle damage assessment, automating underwriting workflows, or designing MLOps frameworks for nationwide deployment, this handbook provides all the insights, methodologies, and practical know-how you need to bring cutting-edge AI to motor insurance—making it the ultimate resource for researchers, practitioners, and industry innovators alike.
Teerapong Panboonyuen
My research focuses on leveraging advanced machine intelligence techniques, specifically computer vision, to enhance semantic understanding, learning representations, visual recognition, and geospatial data interpretation.