Advances in Financial Fraud Detection and Network Security

The field of financial fraud detection and network security is moving towards the adoption of graph-based machine learning approaches and the integration of large language models to improve detection accuracy and contextualization. Researchers are exploring new methods to analyze transactional graphs and identify topological patterns, which can help detect sophisticated criminal behaviors. The use of graph convolutional networks and graph autoencoders is becoming increasingly popular, as they can learn complex behavioral patterns and distinguish between genuine threats and benign activity. Noteworthy papers in this area include:

  • A study that combines Large Language Models with Graph Convolutional Networks to detect fraudulent activities in e-commerce online payment transactions, achieving an accuracy of 0.98.
  • A graph machine learning approach that detects topological patterns in transactional graphs, offering a promising alternative to conventional rule-based detection systems.

Sources

What Does Normal Even Mean? Evaluating Benign Traffic in Intrusion Detection Datasets

Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks

A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs

A Graph-Based Approach to Alert Contextualisation in Security Operations Centres

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