Telecom Fraud Detection Based on Large Language Models: A Multi-Role, Multi-Layer Prompting Strategy

Telecom network fraud continues to evolve, and its textual expressions have become increasingly concealed, making automated detection more challenging. When combined with mainstream prompting strategies, large language models (LLMs) often exhibit unstable performance when handling diverse fraud texts, particularly for long-tail categories and confusing cases where consistent detection is difficult to maintain. To address this limitation, this study proposes a Multi-Role, Multi-Layer (MRML) prompting strategy. The strategy constructs three expert roles—text analysis, business process analysis, and security analysis—and adopts a conditional hierarchical reasoning mechanism to achieve a structured detection process that transitions from rapid binary screening to deep multi-class classification. This design systematically organizes the LLM’s inference steps and enhances its ability to distinguish different types of telecom fraud. Experiments conducted on two public datasets show that the proposed framework significantly outperforms mainstream prompting strategies and surpasses deep learning baselines such as BERT, TextCNN, and Transformer in terms of precision, recall, and F1-score, demonstrating superior performance and robustness. Overall, the results indicate that the proposed prompting strategy provides an effective and practically applicable solution for telecom fraud text detection in real-world scenarios.