This article shares the practical experience of building a Related Transaction Clue Tracking System based on the Neo4j graph database. The system transforms scattered account/transaction/relationship data into an interpretable relationship network, achieving three core capabilities: path query, cycle detection, and account/gang fusion. Through layered strategies, pre-positioned rules, and engineering optimizations, it helps risk control and due diligence scenarios efficiently discover suspicious fund links.
Based on real enterprise experience, this article presents a new paradigm for enterprise AI governance: a unified AI governance and scheduling platform that replaces scattered model integrations. It centralizes multi-model management, intelligent routing and cost governance, and—most importantly—turns in-house AI components and model services into continuously evolving intelligent assets rather than one-off project code.
Building on our company's practical experience, we present Intelligent Site-Wide Profiling and Adaptive Crawling Technology: A two-stage crawling architecture based on large language models, achieving automatic website type recognition, intelligent content modality discrimination, and differentiated strategy routing. Compared to traditional methods, accuracy improves by 25-40%, maintenance costs reduce by 60-80%, supporting intelligent recognition of 10 website types and 7 content modalities.