Quantum Computing for Fraud Detection in Real-Time Payment Systems

The exponential growth of real-time payment (RTP) systems has agitated on the urgency of robust and efficient fraud detection mechanisms. Traditional machine-learning and rule-based systems often face challenges in processing large, very fast-moving transactional data, which results in delayed detection and substantial financial losses. Quantum computing, with its potential to perform parallel computations on complex probability spaces, offers a novel paradigm to enhance fraud detection. This paper centers on the application of quantum machine learning (QML) techniques—specifically Quantum Graph Neural Networks (QGNNs) and Quantum Support Vector Machines (QSVMs)—over RTP networks for detecting fraudulent activities. This paper will practically implement these hybrid classical and quantum model approaches using Qiskit along with Amazon Braket for datasets such as the IEEE-CIS credit card fraud dataset and quantum synthetically created transaction logs. These models will be compared with classical models in terms of accuracy, precision, recall and execution time. The results demonstrate that quantum-enhanced models offer robust detection accuracy and reduced computational latency, far more effectively when faced with severely imbalanced and nonlinear datasets. Specifically, this study buttresses the quantum forensic financial discipline as it brings to light the reality that quantum computing does not just stay in theoretical promise but can indeed be in practical advantages for scalable, real-time systems for the detection of frauds.