{"title":"UCF-PKS: Unforeseen Consumer Fraud Detection With Prior Knowledge and Semantic Features","authors":"Shanyan Lai;Junfang Wu;Chunyang Ye;Zhiwei Ma","doi":"10.1109/TCSS.2024.3372519","DOIUrl":null,"url":null,"abstract":"The utilization of text classification techniques has demonstrated great promise in the field of detecting consumer fraud based on consumer reviews. However, persistent challenges remain in handling large samples at the borders and identifying unforeseen fraud behaviors. To address these challenges, we propose a novel approach that combines a channel biattention convolutional neural network (CNN) with a pretrained language model. Specifically, we propose a similarity computation module for implicitly learning a metric matrix to characterize the similarity between prior knowledge and consumer reviews in vector space. Through this process, the model is able to learn and understand the relationship between prior knowledge and corresponding samples during training, thereby improving its ability to identify unforeseen fraudulent behaviors. Additionally, we propose a channel biattention CNN module to adaptively emphasize the importance of relevant prior knowledge to enhance the model's ability to accurately classify boundary samples. To ensure effective model training, we expand and organize a real-world dataset, reducing noise and increasing the number of fraud samples available for analysis. Experimental results demonstrate that our approach achieves state-of-the-art performance in fraud detection. Notably, our model is capable of detecting unforeseen fraud cases without the need for retraining or fine-tuning, making it highly adaptable and efficient in practical applications.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10475187/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
The utilization of text classification techniques has demonstrated great promise in the field of detecting consumer fraud based on consumer reviews. However, persistent challenges remain in handling large samples at the borders and identifying unforeseen fraud behaviors. To address these challenges, we propose a novel approach that combines a channel biattention convolutional neural network (CNN) with a pretrained language model. Specifically, we propose a similarity computation module for implicitly learning a metric matrix to characterize the similarity between prior knowledge and consumer reviews in vector space. Through this process, the model is able to learn and understand the relationship between prior knowledge and corresponding samples during training, thereby improving its ability to identify unforeseen fraudulent behaviors. Additionally, we propose a channel biattention CNN module to adaptively emphasize the importance of relevant prior knowledge to enhance the model's ability to accurately classify boundary samples. To ensure effective model training, we expand and organize a real-world dataset, reducing noise and increasing the number of fraud samples available for analysis. Experimental results demonstrate that our approach achieves state-of-the-art performance in fraud detection. Notably, our model is capable of detecting unforeseen fraud cases without the need for retraining or fine-tuning, making it highly adaptable and efficient in practical applications.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.