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Text-attributed graph node classification is still a challenge since it needs to reason about the topology structure simultaneously with the free-text semantics. Although graph neural network can perform well on structural propagation,they tend to be blind for the details in the text associated with nodes. On the other hand, LLMs have excellent NLU skills and are weak on structured,multi-hop reasoning over network agents.To address the above gap, in this work we propose FinSCRA, a novel LLM-powered multi-chain reasoning framework to inject domain-aware reasoning capability into a financial LLM with parameters efficient fine-tuning. Specifically, our framework designs a hierarchy of structured reasoning chains (single-hint,parallel, cascaded, and hybrid methods to extract and fuse the semantic signals like sentiment, correlation, and risk signals in the nodes’ text.A fusion layer based on fuzzy logic fuses the results of different reasoning lines for better robustness and explainability.While FinSCRA is generic and can be applied to other types of text-attributed graphs, here we assess its performance on credit risk analysis in supply chain networks,on the task of entity relation extraction, in which entities are related through their financial relation and described with rich text reports; we show experimentally on realworld datasets that our model FinSCRA greatly outperforms graphbased as well as LLM-based baselines,as an accurate and explainable technique to perform node classification over complex networked systems.We release our code and models for further research on LLM-grap.