Intelligent early warning model for financial risks of new energy vehicle enterprises based on deep learning

Engineering notes

Experiments on real-world NEV firm data show that the proposed model achieves higher precision, recall, and interpretability than conventional methods, offering a scalable and policy-aware tool for sustainable financial monitoring.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract The financial stability of new energy vehicle (NEV) firms has become a central concern amid rapid digitalization and the transition toward low-carbon economies. Traditional financial risk models assume fixed structures and linear relationships, which makes them ineffective for handling the complex, time-varying, and policy-sensitive nature of energy firms. To address this challenge, a deep learning–based early warning framework is developed using the Modular Energy Accountability Lattice (MEAL) and the Semantic Policy Fusion Strategy (SPFS). These components jointly encode financial flows and policy dynamics to predict systemic risk. Experiments on real-world NEV firm data show that the proposed model achieves higher precision, recall, and interpretability than conventional methods, offering a scalable and policy-aware tool for sustainable financial monitoring.

Links and sources

• PDF from original source

Comments