## 1 Introduction  
With the rapid development of an information society, cybercrimes, particularly telecom fraud, have become significant global challenges. These crimes are growing at an alarming rate, with the Global Anti-Fraud Alliance and ScamAdviser estimating that 25.5% of the world's population was affected by telecom fraud between August 2022 and August 2023, resulting in over USD 1 trillion in losses [1]. Telecom fraud represents a major obstacle for global law enforcement, and efforts to combat it have intensified [2]. For example, China's National Anti-Fraud Center has undertaken comprehensive measures, including issuing 9.4 million warnings, intercepting billions of fraudulent calls and messages, and preventing losses totaling 328.8 billion yuan [3]. These actions highlight the critical need for continued innovation and vigilance in addressing this problem.  
Academia, governments, and technology companies worldwide are also actively working to combat telecom fraud. Numerous fraud detection models have been proposed [4, 5, 6, 7, 8], and initiatives like the Fake-base-station dataset [9] are helping to advance research in this area. In China, the National Anti-Fraud Center's app and Tencent's Anti-Fraud Mini Program aim to raise awareness and provide tools for combating fraud. However, despite these efforts, telecom fraud remains widespread. The rapid evolution of fraud tactics--such as those linked to global crises like the COVID-19 pandemic--underscores the need for more comprehensive datasets and fraud detection models that are aligned with current societal contexts.  
Existing natural language processing (NLP) models for fraud detection in other domains, such as credit card fraud [10], medical fraud [11], and e-commerce fraud [12; 13], have demonstrated significant success. However, when applied to telecom fraud, these models often fall short due to the text-based nature of telecom fraud and its unique characteristics. To address this gap, there is an urgent need for a specialized NLP model capable of accurately detecting telecom fraud.  
This study proposes a novel NLP-based model specifically designed for telecom fraud detection. The contributions of this research are threefold:  
1. Dataset construction: We introduce a comprehensive five-category dataset, covering the impersonation of customer service, the impersonation of leadership acquaintances, loans, public security fraud, and normal texts. This dataset fills a critical gap in the current research on telecom fraud and provides a resource for further studies.  
2. An innovative loss function: We explore and combine the inconsistency loss function with the cross-entropy function, forming a new loss function framework. This integrated approach improves the fraud detection performance and offers novel insights into model optimization for telecom fraud.  
3. Model design: We develop a specialized telecom fraud detection model based on RoBERTa, enhanced with multi-head attention and residual connections (RoBERTa-MHARC). This model represents a unique research direction in telecom fraud detection and demonstrates superior accuracy in classifying fraudulent texts.  
By addressing these challenges, our work provides an effective tool for combating telecom fraud and contributes to creating a safer digital environment. The paper is structured as follows: Section 2 reviews the related literature on NLP techniques for telecom fraud detection. Section 3 details the proposed model's architecture. Section 4 presents the dataset construction process and the experimental results. Finally, Section 5 concludes with a summary and outlines future research directions.