FinFraudBench：用于金融欺诈检测的异质图基准
FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection
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中文总结 AI 辅助
针对现有金融欺诈检测基准与实际金融系统不匹配的问题，本文提出异质图基准FinFraudBench，构建两个大规模异质金融图数据集并建立标准化评估协议，为相关研究提供支撑。
中文摘要 AI 辅助
数字金融系统日益复杂，已将金融欺诈检测从孤立的交易分类转变为对相互关联金融实体的关系风险推理，这一转变推动了基于图的欺诈检测方法，这类模型通过利用客户、银行卡、商户、类别和位置之间的依赖关系来识别欺诈节点。然而，尽管基于图的方法发展迅速，现有的公开基准在两个重要方面与现实世界的金融系统不匹配：其一，它们常将金融生态系统简化为同质或单节点类型的多关系图，未能保留金融数据的多实体和多关系特性；其二，它们很少提供具有极端类别不平衡、标签有限等现实运行条件的大规模异质金融图数据集，导致难以评估现有方法的实际有效性。为解决这些不足，本文提出了用于金融欺诈检测的异质图基准FinFraudBench。该基准包含两个异质图数据集（CreditCard-Fraud和BankTrans-Fraud），最多有899万个节点和8923万条带类型的有向边，每个数据集保留六种金融实体类型、14种有向边类型，以及反映部署约束的自然欺诈率。基于这些数据集，本文建立了涵盖排序和对不平衡敏感的分类指标的标准化评估协议，并评估了代表性基线方法，大量实验得出了关于现有方法局限性的实证见解，并为未来研究提出了有前景的方向，FinFraudBench可在指定网址获取。
英文摘要
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 https://anonymous.4open.science/r/FinFraudBench-B002.
发表机构
- HKUST-GZ（香港科技大学（广州））
- Jilin University（吉林大学）
- Ant Group（蚂蚁集团）
机构由 AI 辅助整理，请以论文原文为准。
