Influence Evaluation of Telecom Fraud Case Types Based on ChatGPT

doi:10.3778/j.issn.1673-9418.2306044

Abstract

Abstract: At present, telecommunications fraud crimes are on the rise, posing a serious threat to the safety of people??s property. In order to optimize anti-fraud strategies, objectively and accurately analyze the trends and characteristics of different types of telecommunications fraud cases, and determine the most influential criminal methods, a ChatGPT based telecommunications fraud case type impact assessment method is proposed. By utilizing a knowledge graph, the content of the case text is structured, and the methods of telecommunications fraud are quantified by taking the time of the incident, the amount involved, and the number of individuals involved as factors to evaluate the impact of the case. Firstly, ChatGPT is used to preprocess and extract knowledge from the text corpus of telecommu-nications fraud cases through multiple rounds of Q&A, in order to quickly and timely construct a case knowledge graph in the field of telecommunications fraud with low resources. Based on the knowledge graph, various factors such as incident time, amount involved, and the number of involved parties are statistically analyzed, and the impact of different types of cases is abstracted into influencing factors. The influencing factors are used to depict the trend and characteristics of incidents, to conduct comprehensive analysis and judgment. This paper analyzes existing case data and calculates the impact factors of case types, obtaining the changes in impact factors of different case types, verifying the scientific and effective calculation methods of impact factors, and providing a new method for the evaluation of telecommunications fraud types. Combining the advantages of ChatGPT and knowledge graph helps to timely grasp the trend of case development and changes, provides strong support and guidance to combat teleco-mmunications fraud, and is of great significance for protecting public property safety and social stability.

Key words: Telecom fraud, ChatGPT, knowledge graph, impact assessment

摘要： 当前电信诈骗犯罪呈高发态势，严重威胁人民群众财产安全，为了优化反诈策略、客观准确分析不同类型电信诈骗案件的发案趋势和发案特征，确定影响力较大的犯罪手段和方式，提出一种基于ChatGPT的电信诈骗案件类型影响力评估方法。借助知识图谱使案件文本内容结构化，并将案发时间、涉案金额、涉案事主人数作为评估案件影响力的因素，对电信诈骗方式进行量化。首先利用ChatGPT通过多轮问答的形式对电信诈骗案件文本语料进行数据预处理和知识抽取，低资源、快速及时地构建电信诈骗领域的案件知识图谱，并基于知识图谱统计分析案发时间、涉案金额、涉案事主人数等各类因素，把不同案件类型的影响抽象为影响因子，用影响因子刻画案发趋势与发案特征，以进行综合分析研判。通过对现有案例数据进行分析，计算案件类型影响因子，得到了不同案件类型的影响因子变化，验证了影响因子计算方法的科学性与有效性，为电信诈骗类型的评估提供了一种新的方法和思路。结合ChatGPT与知识图谱的优势，有助于及时把握案件发展变化趋势，为打击电信诈骗提供有力的支持和指导，对于保护公众的财产安全和社会的稳定具有重要意义。

关键词: 电信诈骗, ChatGPT, 知识图谱, 影响评估

PEI Bingsen, LI Xin, WU Yue. Influence Evaluation of Telecom Fraud Case Types Based on ChatGPT[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2413-2425.

裴炳森, 李欣, 吴越. 基于ChatGPT的电信诈骗案件类型影响力评估[J]. 计算机科学与探索, 2023, 17(10): 2413-2425.

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