Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

Abstract

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

Credit Card Fraud Detection Using Hybrid Machine Learning Algorithms

“…In this research, a hybrid feature selection model is used, which combines both filter and wrapper techniques. As shown in figure [2], some group features showed high correlation results.…”

“…According to the No Free Lunch Theorem, no single model or algorithm can handle all classification problems. Furthermore, each different algorithm has its advantages and disadvantages as illustrated in Table [2]. Consequently, the combination of several algorithms exploits the weaknesses of one, such as overfitting.…”

“…The dataset is highly unbalanced, the positive class (frauds) account for 0.17% of all transactions. Most banks and financial firms use rule-based systems, in which an expert will use historical fraud data to define a set of rules, and a system will raise an alarm if a new transaction match one of the rules [1,2]. The main limitations of this manual process are that it is reactive, lacks flexibility and consistency as well as the fact that it is time-consuming [3].…”

Anti‐money laundering and financial fraud detection: A systematic literature review

“…Chen et al (2018) aimed to provide a survey of methods that applied machine‐learning algorithms to detect suspicious transactions. Kurshan and Shen (2020) reviewed the literature on graphical methods for detecting financial fraud, which mapped existing procedures and described that the combination of graphical methods with algorithms provides better fraud detection. Kurshan et al (2020) explored fraud detection methods based on graphical applications (based on machine learning and artificial intelligence concepts), addressing fraud in general and combining existing techniques with graphical analysis.…”

A Review on Graph Neural Network Methods in Financial Applications

“…Including payment fraud, identity theft, financial scam and insurance fraud, financial fraud has a variety of types and has an increasing trend (Kurshan and Shen, 2020). Observing that fraudsters tend to have abnormal connectivity with other users, there is a trend to present users' relations in a graph and thus, the fraud detection task could be formulated as a node classification task.…”