发表机构
Arab Open University（阿拉伯开放大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对金融欺诈检测难题，提出FraudShield AI混合框架，融合LSTM网络与图拓扑特征，设计网络中心特征，用焦点损失和动态阈值机制，在PaySim数据集实验中性能超基线，消融研究验证组件互补性。
AI 中文摘要
金融机构在检测复杂的洗钱模式（如小额分散和分层）上面临重大挑战，因为存在极端数据不平衡（欺诈率0.13%）和不断演变的对抗性规避策略。本文提出了FraudShield AI，这是一个将长短期记忆（LSTM）网络与手工制作的图拓扑特征相结合的混合框架，以捕捉时间序列和结构关系上下文。通过设计以网络为中心的特征，该系统将检测范式从孤立交易分析转变为网络级取证。使用焦点损失目标来解决类别不平衡问题，并引入动态阈值机制来提高对低价值小额分散攻击的弹性。在PaySim数据集上的实验评估表明，所提出的混合模型在精度、召回率和F1分数方面大大优于逻辑回归和XGBoost基线，特别是在难以检测的微交易欺诈模式上。消融研究证实了时间和拓扑组件的互补贡献。
英文摘要
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.
Comments6 pages, 12 figures
