Leveraging Graph Neural Networks for Improved Fraud Detection in Financial Transactions

Fraud detection has been pressing with the elevating rate of fraud activities in financial transactions. Traditional methods often fail to identify complex patterns within large-scale data. The paper reviews Graph Neural Networks (GNNs) and their applications for enhancing fraud detection by graphical modeling of financial transactions, which creates an environment for capturing intricate connections among entities. We provide a GNN-based framework experiment with a comprehensive dataset, representing accuracy with better precision against traditional models. In this paper, we will present some solutions to overcome the challenges when deploying real-world GNNs into the banking system and open up the potential for GNNs to become one of the critical tools in the prevention of unauthorizedtransactions for financial institutions. Also, we will be discussing libraries such as NumPy, Pandas, Seaborn, Matplotlib, Warnings, Scikit- Learn etc. for carefully visualizing data and for carefully analyzing the performance of our model. For choosing a powerful methodology, the exhibition of different calculations is looked at, for example, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting Classifier for distinguishing bogus exchanges. Further, we will also be displaying numerous analysis of fraud indicators, anomalies, and transaction data which helps in deeply understanding transaction behaviors.