Cross Domain Fraud Detection

Cross-domain fraud detection aims to build robust models that can identify fraudulent activities across diverse data sources and contexts, overcoming challenges like data imbalance and evolving fraud patterns. Current research emphasizes developing methods for knowledge transfer between domains, often employing deep learning architectures adapted for sequential data and incorporating techniques to improve fairness and interpretability. This work is crucial for enhancing the security and reliability of various online platforms and financial systems, while also advancing the understanding of fairness and generalization in machine learning.