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

By T. Pourhabibi, K. Ong, B. Kam, Yee Ling Boo. Decision Support Systems, 2020

401 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fffbf0cbafa24f17fe5db8582efaa3406ebd08a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qing He
2. Hui Xiong
3. Jia Wu
4. Quan Z. Sheng
5. Chuan Zhou
6. Samuel A. Assefa
7. Shan Xue
8. Zidi Qin

In Statistical and Nonlinear Physics

1. Qing He
2. Hui Xiong
3. Jia Wu
4. Quan Z. Sheng
5. Chuan Zhou
6. Samuel A. Assefa
7. Shan Xue
8. Xiaoxiao Ma

Citations in the corpus

• Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (2021, background): “Other related researches could be referred to [33].”
• Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, background): “Notable applications include modelling the interdependencies between financial institutions [16, 42] and modeling payments networks for anti-money laundering [36] and fraud [66].”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “Other surveys are dedicated to particular applications of graph anomaly detection, such as computer network intrusion detection and anomaly detection in online social networks, e.g., [3], [6], [19], and [50], [51], [52], [53], [54].”