Financial Fraud Detection

Financial fraud detection aims to identify fraudulent activities within financial transactions and networks, primarily focusing on improving accuracy and efficiency while addressing data privacy concerns. Current research emphasizes advanced machine learning models, including graph neural networks (GNNs), quantum machine learning (QML) algorithms, and federated learning (FL) frameworks, to analyze complex transaction patterns and user behavior across diverse data sources. These advancements are crucial for mitigating financial losses, enhancing the security of financial systems, and promoting trust in digital transactions, with a growing focus on explainable AI (XAI) to improve transparency and accountability.