Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to identify fraudulent patterns. This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud by analysing relationships between financial accounts. The primary objective is to compare the performance of traditional features with the addition of PPR in fraud detection models. Results indicate that integrating PPR enhances the model's predictive power, surpassing the baseline model. Additionally, the PPR feature provides unique and valuable information, evidenced by its high feature importance score. Feature stability analysis confirms consistent feature distributions across training and test datasets.

翻译：在线交易欺诈给企业和消费者带来重大挑战，并可能导致巨额经济损失。传统的基于规则的系统难以跟上不断变化的欺诈手段，导致高误报率和漏检率。机器学习技术通过利用历史数据识别欺诈模式，提供了一种有前景的解决方案。本文探索使用个性化PageRank（PPR）算法，通过分析金融账户间的关系来捕捉欺诈的社会动态。主要目标是比较传统特征与加入PPR特征后在欺诈检测模型中的性能表现。结果表明，整合PPR增强了模型的预测能力，超越了基线模型。此外，PPR特征凭借其较高的特征重要性得分，提供了独特且有价值的信息。特征稳定性分析证实了训练集与测试集之间的特征分布具有一致性。