融合时序与图结构特征，提升金融欺诈检测鲁棒性
Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience
- 用LSTM捕捉交易时序，结合图中心性等特征建模网络关系
- 在PaySim数据集上实现92.3%召回率，对小额洗钱攻击有效
- 适合风控系统研发者和反欺诈算法工程师参考
金融机构在检测复杂洗钱模式（如分拆转账、分层）时面临巨大挑战，主要源于极端数据不平衡（欺诈率仅0.13%）及持续演进的对抗规避策略。本文提出FraudShield AI，一种融合长短期记忆网络（LSTM）与人工设计图拓扑特征的混合框架，以同时捕捉交易时序与结构关联信息。通过构造包括PageRank中心性、入度动态变化及自定义流量比率在内的网络中心特征，将检测范式从孤立交易分析转向网络级取证。采用焦点损失缓解类别不平衡问题，并引入动态阈值机制增强对低价值分拆攻击的韧性。在PaySim数据集上的实验表明，该混合模型在精确率、召回率和F1分数上显著优于逻辑回归与XGBoost基线，尤其在难以识别的小额交易欺诈模式上表现突出。消融实验验证了时序与拓扑组件的互补贡献。
原文摘要 · Abstract (English)
Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.13% fraud rate) and evolving adversarial evasion tactics. This paper proposes FraudShield AI, a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with hand-crafted Graph Topological Features to capture both temporal sequences and structural relational context. By engineering network-centric features including PageRank Centrality, In-Degree dynamics, and a custom Flow Ratio, the system shifts the detection paradigm from isolated transaction analysis to network-level forensics. A Focal Loss objective is used to address class imbalance, and a dynamic thresholding mechanism is introduced to improve resilience against low-value smurfing attacks. Experimental evaluation on the PaySim dataset shows that the proposed hybrid model substantially outperforms Logistic Regression and XGBoost baselines in Precision, Recall, and F1-Score, particularly on hard-to-detect micro-transaction fraud patterns. An ablation study confirms the complementary contribution of both the temporal and topological components.
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