Rodrigo Ying

Submitted: 07 February 2026 You are already at the latest version This work targets open-world credit risk identification where few labeled samples and emerging fraud scenarios coexist. It proposes a unified detection framework that integrates meta learning and domain adaptation. The goal is to improve rapid adaptation and cross-scenario robustness in dynamic environments. The method uses a shared encoder for representation learning over multiple source transaction features.

Submitted: 30 December 2025 You are already at the latest version This paper addresses the challenges in traditional enterprise performance analysis, including complex multi-source data structures, ambiguous indicator correlations, and poor decision interpretability. It proposes an enterprise performance optimization decision model that integrates knowledge graphs with causal inference.

Submitted: 15 December 2025 You are already at the latest version This study proposes a prediction model based on a dynamic spatiotemporal causal graph neural network to address the challenges of complex dynamic dependencies, strong structural correlations, and ambiguous causal relationships in corporate revenue forecasting. The model constructs a time-varying enterprise association graph, where enterprises are represented as nodes and industry or supply chain relationships as edges.