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.

通过分析金融账户之间的关系，本研究探讨了使用个性化 PageRank 算法来捕捉欺诈的社会动态。结果表明，将个性化 PageRank 算法融入欺诈检测模型可以提高预测能力，并且该算法的重要性得分较高，特征稳定性分析确认训练和测试数据集之间的特征分布一致。