Fraud detection: A systematic literature review of graph-based anomaly detection approaches
Tahereh Pourhabibi, Kok‐Leong Ong, Booi Kam, Yee Ling Boo
Abstract
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.
Topics & Concepts
CredibilityAnomaly detectionComputer scienceGraphKey (lock)Power graph analysisData miningData scienceComputer securityTheoretical computer sciencePolitical scienceLawImbalanced Data Classification TechniquesAnomaly Detection Techniques and ApplicationsComplex Network Analysis Techniques