Fraud detection: A systematic literature review of graph-based anomaly detection approaches

作者:

Highlights:

• A comprehensive systematic literature review of graph-based anomaly detection (GBAD) on fraud detection accomplished.

• This paper bridges the existing gaps and encourages data scientists to embark on new empirical research in this domain.

• It opens up new potential application areas that GBAD techniques could be quite practical.

• It proposes a classification framework as a foundation to get a more in-depth insight into GBAD methods for fraud detection.

摘要

Graph-based anomaly detection (GBAD) approaches are among the most popular techniques used to analyze connectivity patterns in communication networks and identify suspicious behaviors. Given the different GBAD approaches proposed for fraud detection, in this study, we develop a framework to synthesize the existing literature on the application of GBAD methods in fraud detection published between 2007 and 2018. This study aims to investigate the present trends and identify the key challenges that require significant research efforts to increase the credibility of the technique. Additionally, we provide some recommendations to deal with these challenges.

论文关键词:Fraud detection,Graph-based anomaly detection,Graph data,Systematic literature review,Social network,Big data analytics

论文评审过程:Received 5 August 2019, Revised 13 April 2020, Accepted 14 April 2020, Available online 17 April 2020, Version of Record 1 May 2020.

论文官网地址:https://doi.org/10.1016/j.dss.2020.113303