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Graph Neural Networks (GNNs) have demonstrated exceptional performance in modeling structural dependencies within networked data. However, in complex decision-making environments, structural information alone often fails to capture the latent semantic logic and domain-specific heuristics. While Large Language Models (LLMs) excel in semantic reasoning, their integration with graph-structured data remains loosely coupled in existing literature. This paper proposes CSSA, a novel Cross-modal Semantic-Structural Alignment framework that synergizes the zero-shot reasoning of LLMs with the topological aggregation of GNNs through a contrastive learning objective. Specifically, we treat node attributes as semantic prompts for LLMs to distill high-level "risk indicators," while a GNN branch encodes the local neighborhood topology. A cross-modal alignment layer is then introduced to minimize the representational gap between semantic intent and structural behavior. We evaluate CSSA on a massive dataset of 2.84 million online transaction records. Experimental results demonstrate that CSSA achieves a superior F1-score and AUC compared to state-of-the-art GNNs, particularly in scenarios characterized by extreme class imbalance and covert adversarial patterns.