This study constructs a comprehensive economic forecasting framework integrating LSTM, XGBoost, and Dynamic Factor Models. Through comparative analysis of different models in GDP time series forecasting, experimental results demonstrate that multi-model fusion methods effectively improve prediction accuracy, providing scientific basis for economic policy formulation.
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.