2024/02/14 by Catayoun Azarm, Azarm, Catayoun, Erman Acar +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Economics and business #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Risk Management (q-fin.RM)
paper · pdf · doi:10.48550/arxiv.2402.09495
openalex publication_date 2024/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.