FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection

[2608.15177] FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.15177 (cs) [Submitted on 15 Aug 2026] Title: FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection Authors: Yixuan Chen , Hongyu Zhan , Jie Sheng , Weiyu Han , Shuai Chen , Tianyi Zhang , Xiao Tan , Jun Xia View a PDF of the paper titled FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection, by Yixuan Chen and 7 other authors View PDF HTML (experimental) Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities. This shift has motivated graph-based fraud detection, where models identify fraudulent nodes by exploiting dependencies among customers, cards, merchants, categories, and locations. However, despite rapid progress in graph-based methods, existing public benchmarks remain misaligned with real-world financial systems in two important aspects. First, they often simplify financial ecosystems into homogeneous or single-node-type multi-relational graphs, failing to preserve the multi-entity and multi-relational nature of financial data. Second, they rarely provide large-scale heterogeneous financial graph datasets with realistic operating conditions such as extreme class imbalance and limited label availability, making it difficult to assess the practical effectiveness of current methods. To address these gaps, we present FinFraudBench, a heterogeneous graph benchmark for financial fraud detection. FinFraudBench contains two heterogeneous graph datasets (CreditCard-Fraud and BankTrans-Fraud) with up to 8.99M nodes and 89.23M directed typed edges. Each dataset preserves six financial entity types, fourteen directed edge types, and natural fraud rates that mirror deployment constraints. With these datasets, we establish a standardized evaluation protocol covering both ranking and imbalance-sensitive classification metrics, and evaluate representative baselines. Extensive experiments yield empirical insights into current methods' limitations and suggest promising avenues for future research. FinFraudBench is available at this https URL . Comments: 16 pages, 7 figures Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15177 [cs.LG] (or arXiv:2608.15177v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15177 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yixuan Chen [ view email ] [v1] Sat, 15 Aug 2026 11:34:51 UTC (1,311 KB) Full-text links: Access Paper: View a PDF of the paper titled FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection, by Yixuan Chen and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI 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

• FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection#611123
• TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering#609920
• Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation#386862
• Financial fraud detection model based on dual-layer knowledge graph#680858
• Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling#1063881