标题： DynBERG：基于动态BERT的图神经网络用于金融欺诈检测 显示英文标题

标题： DynBERG: Dynamic BERT-based Graph neural network for financial fraud detection

摘要： 金融欺诈检测对于维护金融系统的完整性至关重要，尤其是在去中心化环境如加密货币网络中。 尽管图卷积网络（GCNs）被广泛用于金融欺诈检测，但由于基于Transformer的架构能够缓解过度平滑等问题，图Transformer模型如Graph-BERT正逐渐受到关注。 Graph-BERT专为静态图设计，并主要在具有无向边的引用网络上进行评估。 然而，金融交易网络本质上是动态的，具有不断变化的结构和表示资金流动的有向边。 为了解决这些挑战，我们引入了DynBERG，这是一种新颖的架构，将Graph-BERT与门控循环单元（GRU）层结合，以捕捉多个时间步的时序演变。 此外，我们修改了底层算法以支持有向边，使DynBERG非常适合动态金融交易分析。 我们在Elliptic数据集上评估了我们的模型，该数据集包括比特币交易，包括一次重大加密货币市场事件——暗网市场关闭期间的所有交易。 通过评估该事件前后DynBERG的鲁棒性，我们分析了其适应影响交易行为的重大市场变化的能力。 我们的模型与最先进的动态图分类方法如EvolveGCN和GCN进行了基准比较，表现出优越的性能，在市场关闭前优于EvolveGCN，在事件后超越了GCN。 此外，消融研究突显了融入时间序列深度学习组件的关键作用，展示了GRU在建模金融交易时序动态方面的有效性。 显示英文摘要

摘要： Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BERT are gaining prominence due to their Transformer-based architecture, which mitigates issues such as over-smoothing. Graph-BERT is designed for static graphs and primarily evaluated on citation networks with undirected edges. However, financial transaction networks are inherently dynamic, with evolving structures and directed edges representing the flow of money. To address these challenges, we introduce DynBERG, a novel architecture that integrates Graph-BERT with a Gated Recurrent Unit (GRU) layer to capture temporal evolution over multiple time steps. Additionally, we modify the underlying algorithm to support directed edges, making DynBERG well-suited for dynamic financial transaction analysis. We evaluate our model on the Elliptic dataset, which includes Bitcoin transactions, including all transactions during a major cryptocurrency market event, the Dark Market Shutdown. By assessing DynBERG's resilience before and after this event, we analyse its ability to adapt to significant market shifts that impact transaction behaviours. Our model is benchmarked against state-of-the-art dynamic graph classification approaches, such as EvolveGCN and GCN, demonstrating superior performance, outperforming EvolveGCN before the market shutdown and surpassing GCN after the event. Additionally, an ablation study highlights the critical role of incorporating a time-series deep learning component, showcasing the effectiveness of GRU in modelling the temporal dynamics of financial transactions.