Inductive Link Prediction Banking Fraud Detection System Using Homogeneous Graph-Based Machine Learning Model

Graph machine learning and fraud detection systems are growing and popular today. Fraud detection systems have been widely used as a tool to detect potentially fraudulent transactions. Fraud detection systems can be used to determine patterns of transactions that are suspected of being criminal transactions. Graph machine learning development can be implemented in anything that can be represented in graph form. The banking fraud detection system can be implemented in graph form by connecting customers who have made transactions with other customers or customer transactional activities. From the graph that has been formed, predictions will be made so that new transactions can be classified as fraudulent transactions or not by connecting these transactions with the graphs that have been made. The experimental results show that the graph-based fraud detection model produces better performance than the tree-based fraud detection model, but with a longer inference time.