On the Potential of Network-Based Features for Fraud Detection

arXiv:2402.09495v1 Announce Type: cross
Abstract: 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 …

abstract arxiv businesses challenges consumers cs.ai cs.lg data detection false features financial fraud fraud detection historical data losses machine machine learning machine learning techniques network positive q-fin.rm solution struggle systems tactics type