Credit Card Fraud
Credit card fraud detection aims to identify and prevent unauthorized transactions, minimizing financial losses and protecting consumers. Current research heavily utilizes machine learning, particularly deep learning architectures like graph neural networks and transformers, along with ensemble methods and techniques to address class imbalance inherent in fraud datasets. These advancements are crucial for improving the accuracy and efficiency of fraud detection systems, impacting both financial institutions and the broader security landscape. Furthermore, research is exploring explainable AI methods to increase transparency and trust in these critical systems.
Papers
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering
Ahmed N. Bakry, Almohammady S. Alsharkawy, Mohamed S. Farag, Kamal R. Raslan
On the Potential of Network-Based Features for Fraud Detection
Catayoun Azarm, Erman Acar, Mickey van Zeelt
ScamSpot: Fighting Financial Fraud in Instagram Comments
Stefan Erben, Andreas Waldis