Fraud Detection

Fraud detection research aims to develop robust and accurate methods for identifying fraudulent activities across various domains, primarily focusing on minimizing false positives while maximizing detection rates. Current research emphasizes the use of machine learning models, including graph neural networks, transformers, and gradient boosted decision trees, often coupled with techniques to address class imbalance and data scarcity, such as SMOTE and generative models. These advancements are crucial for mitigating financial losses, enhancing security in online transactions and payment systems, and improving the overall reliability of data-driven decision-making in various sectors. Furthermore, there's a growing focus on model explainability and fairness to ensure transparency and prevent discriminatory outcomes.