[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82389-en":3,"doc-seo-82389-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82389,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","TSAI MetaFraud 基准数据集：元宇宙中的金融欺诈交易与行为风险检测","Metaverse platforms create virtual economies where fraud, bot activity, and illicit financial behavior are increasingly difficult to detect and evaluate. Existing datasets often isolate user behavior, authentication, or transactions, which restricts reproducible multimodal fraud detection research. TSAI-MetaFraud provides a multimodal, multi-task benchmark that fuses behavioral signals, transactional records, and graph-structured relationships, including realistic fraud and automated bot scenarios. It defines tasks such as transaction fraud detection, cross-modal node classification, temporal link prediction, and weakly supervised fraud detection, with baseline evaluations using ML and graph neural networks.","TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems  \nRefat Ishrak Hemel, Ehsan Hallaji, and Roozbeh Razavi-Far  \nTrustworthy and Secure AI Lab (TSAI Lab), Faculty of Computer Science, University of New Brunswick, Canada  \n{refatishrak.hemel, e.hallaji, [roozbeh.razavi-far](roozbeh.razavi-far}@unb.ca)[}](roozbeh.razavi-far}@unb.ca)[@unb.ca](roozbeh.razavi-far}@unb.ca)  \narXiv :2607 .09528v 1 [ cs .LG] 10 Jul 2026  \nAbstract—The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics, existing datasets typically focus on user behavior, authentication, or financial transactions in isolation, limiting the development and reproducible evaluation of multimodal fraud detection methods. To address this gap, we present TSAI-MetaFraud, a multimodal, multi-task benchmark dataset for fraud analytics in virtual economies. TSAI-MetaFraud integrates behavioral, transactional, and graph-structured information while incorporating realistic fraud and automated bot scenarios. We define benchmark tasks including transaction fraud detection, cross-modal node classification, temporal link prediction, and weakly supervised fraud detection, and provide baseline evaluations using machine learning models and graph neural networks. By jointly capturing behavioral activity, financial interactions, and relational structure within a unified virtual economy, TSAI-MetaFraud provides a benchmark for advancing multimodal learning, graph mining, fraud analytics, and trustworthy AI in emerging metaverse ecosystems.  \nIndex Terms—Metaverse, fraud detection, graph mining, behavioral analytics, virtual economies.  \nI. INTRODUCTION  \nThe rapid development of metaverse technologies is transforming virtual environments into persistent digital ecosystems where users socialize, collaborate, trade virtual assets, and participate in increasingly sophisticated economic activities. Platforms supporting virtual commerce, digital ownership, and avatar-based interactions are expected to play a significant role in future online economies [1] . As these environments evolve, concerns regarding trust, security, and financial integrity have become increasingly important [2], [3] . Similar to traditional financial systems, metaverse economies are vulnerable to fraudulent transactions, automated bot activity, account manipulation, identity abuse, and coordinated malicious behavior [3], [4] .  \nRecent advances in machine learning, graph mining, and fraud analytics have demonstrated considerable success in detecting illicit activities in domains such as banking, ecommerce, and cryptocurrency networks [5], [6] . However, applying these techniques to metaverse environments remains challenging due to the scarcity of publicly available datasets. Existing fraud datasets primarily focus on transaction net  \nworks, while metaverse datasets often emphasize user interDataset available at: [https://github.com/tsai-unb/MetaFraud](https://github.com/tsai-unb/MetaFraud).  \nactions, virtual environments, or visual content. Consequently, researchers lack realistic benchmarks that jointly capture user behavior, financial activity, and interaction networks within a unified virtual-world setting.  \nThe metaverse introduces unique characteristics that further distinguish it from conventional fraud detection domains. Financial transactions are closely intertwined with avatar behavior, social interactions, and movement patterns [3] . Malicious actors may exploit both behavioral and financial channels simultaneously, making it necessary to analyze heterogeneous information sources rather than relying on transactions alone [7] . Furthermore, the dynamic and evolving nature of virtual environments creates complex network structures and temporal dependencies that are not adequately ","cbCaimeP1QQyHlGr","https://ap.wps.com/l/cbCaimeP1QQyHlGr","pdf",4335405,1,10,"English","en",105,"# Introduction\n# TSAI-MetaFraud Overview\n## Benchmark Tasks and Baselines\n## Dataset Comparison","[{\"question\":\"What is TSAI-MetaFraud designed to solve in metaverse fraud research?\",\"answer\":\"It addresses the lack of public, realistic benchmarks that jointly capture behavioral activity, financial transactions, and interaction networks for fraud detection in metaverse ecosystems.\"},{\"question\":\"What data modalities and structures does TSAI-MetaFraud include?\",\"answer\":\"It integrates behavioral and financial aspects using avatar interactions, transaction records, biometric attributes, and graph-structured relationships.\"},{\"question\":\"Which benchmark tasks are defined by TSAI-MetaFraud?\",\"answer\":\"The dataset 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