Fraud Pattern

Fraud detection research focuses on identifying and preventing increasingly sophisticated fraudulent activities across diverse sectors, from finance and telecommunications to healthcare and cryptocurrency markets. Current research emphasizes the use of machine learning, particularly graph neural networks and deep learning models, often incorporating multimodal data fusion and techniques like cohort analysis to improve detection accuracy and robustness against evolving fraud patterns. This work is crucial for mitigating substantial financial losses and protecting consumers, while also advancing the development of explainable AI methods for improved transparency and trust in fraud detection systems. The ongoing challenge lies in adapting to the dynamic nature of fraud and addressing issues like data imbalance and the lack of publicly available datasets for model training and validation.