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Graph Learning-Empowered Financial Fraud Detection: Progress and Future Directions
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DOI：10.34133/icomputing.0146.png)
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Abstract
En 中文
Financial fraud detection is an important task in ensuring the integrity and security of financial systems. In recent years, it has been shown that graph learning, which utilizes the relational structure of data, can considerably enhance the detection of fraudulent financial activity by accurately modeling the complex patterns and relationships inherent in financial transactions. In this work, we provide a comprehensive survey on the emerging application of graph learning techniques in detecting and combating financial fraud that can serve as a guidepost for researchers and practitioners interested in leveraging the power of graph learning to create a safer, more secure financial environment. Specifically, we start by introducing the fundamental concepts of graph learning, outlining their unique advantages over traditional machine learning techniques in the context of financial fraud detection. It is worth mentioning that graph learning techniques enable end-to-end training from relational data input to fraud prediction, eliminating the need for additional feature engineering. We then delve into a systematic review of the recent advancements and methodologies in applying graph learning to various financial fraud scenarios, such as credit card fraud, insurance fraud, and money laundering. Furthermore, we provide unique insights regarding several critical challenges, such as data privacy, scalability, and the dynamic nature of financial networks, that are faced when implementing graph learning models in real-world financial ecosystems. We show that practical applications of graph learning still suffer from computational complexity and lack of interpretability, and we offer a forward-looking perspective on potential research directions and improvements that can boost the effectiveness of graph learning applied to financial fraud detection.
Keywords:
Graph Learning
Financial Fraud Detection
Relational Data
Machine Learning
Fraud Prediction
Journal
I
IF:
3.7
Papers:
13
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0