Large Language Models for Risk Detection in E-commerce: Reliability, Semantic Alignment, and Managerial Insights

Abstract The increasing complexity of global e-commerce supply chains underscores the need for automated, context-aware risk monitoring systems capable of interpreting large volumes of unstructured information. Although Large Language Models (LLMs) have shown strong performance across natural language processing tasks, their application to real-world supply chain risk detection remains limited. This study presents a novel, manually annotated dataset of 121 business news articles related to five major steel companies, using the Cambridge Risk Taxonomy. Leveraging this dataset, we evaluate two state-of-the-art LLMs in a multi-label risk classification task using few-shot prompting. The results demonstrate that LLMs can approximate human annotation, though challenges persist in detecting domain-specific risks such as Geopolitical threats and in avoiding label overgeneration. Beyond classification, we further assess the capacity of LLMs to generate managerial risk summaries. We show that summaries derived from model-predicted risks exhibit strong semantic alignment to summaries generated from human annotations, highlighting the potential of LLMs to support executive-level risk interpretation. Overall, this study contributes the first publicly available dataset of fine-grained, hierarchical risk annotations in an e-commerce supply chain context and provides empirical evidence on the opportunities and limitations of LLMs for both analytical and narrative forms of automated risk assessment.