TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering

[2604.17420] TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2604.17420 (cs) [Submitted on 19 Apr 2026 ( v1 ), last revised 25 Jun 2026 (this version, v2)] Title: TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering Authors: Keyang Chen , Mingxuan Jiang , Yongsheng Zhao , Zeping Li , Zaiyuan Chen , Weiqi Luo , Zhixin Li , Sen Liu , Yinan Jing , Guangnan Ye , Xihong Wu , Hongfeng Chai View a PDF of the paper titled TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering, by Keyang Chen and 11 other authors View PDF HTML (experimental) Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction-graph datasets suffer from two pervasive limitations: (i) they provide sparse node-level semantics beyond anonymized identifiers, and (ii) they rely on template-driven anomaly injection, which biases benchmarks toward static structural motifs and yields overly optimistic assessments of model robustness. We propose TransXion, a benchmark ecosystem for Anti-Money Laundering (AML) research that integrates profile-aware simulation of normal activity with stochastic, non-template synthesis of illicit this http URL jointly models persistent entity profiles and conditional transaction behavior, enabling evaluation of "out-of-character" anomalies where observed activity contradicts an entity's socio-economic context. The resulting dataset comprises approximately 3 million transactions among 50,000 entities, each endowed with rich demographic and behavioral attributes. Empirical analyses show that TransXion reproduces key structural properties of payment networks, including heavy-tailed activity distributions and localized subgraph structure. Across a diverse array of detection models spanning multiple algorithmic paradigms, TransXion yields substantially lower detection performance than widely used benchmarks, demonstrating increased difficulty and realism. TransXion provides a more faithful testbed for developing context-aware and robust AML detection methods. The dataset and code are publicly available at this https URL . Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) Cite as: arXiv:2604.17420 [cs.LG] (or arXiv:2604.17420v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.17420 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), 8707-8718, 2026 Related DOI : https://doi.org/10.1145/3770855.3817550 Focus to learn more DOI(s) linking to related resources Submission history From: Mingxuan Jiang [ view email ] [v1] Sun, 19 Apr 2026 13:02:24 UTC (6,613 KB) [v2] Thu, 25 Jun 2026 12:16:00 UTC (6,612 KB) Full-text links: Access Paper: View a PDF of the paper titled TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering, by Keyang Chen and 11 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.SI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling#1063881
• Tracing the Shadows: Automatic Tracking and Analysis of Crypto Money Laundering via Transaction Semantic Analysis#386743
• MONEY LAUNDERING CRIME#194216
• An Interpretable Approach to Money Laundering Detection in Transaction Graphs using Pass-Through Templates#1034533
• MONEY LAUNDERING AS A TRANSNATIONAL FINANCIAL CRIME: INTERNATIONAL LEGAL RESPONSES AND MODERN CHALLENGES#267466