Abstract:

Fraudulent transaction identification is crucial in today's digital world, and machine learning has proven effective in addressing this challenge. However, existing systems often detect fraudulent activities post-occurrence, lacking real-time efficacy. Additionally, the highly imbalanced nature of fraud data complicates traditional machine learning approaches. To overcome these limitations, we propose a novel fraud detection framework using Quantum-Enhanced Support Vector Machines (QSVM). Leveraging quantum annealing solvers, the QSVM exhibits remarkable improvements in both speed and accuracy when applied to a highly imbalanced bank loan dataset, while maintaining competitive performance on a moderately imbalanced Israel credit card transaction dataset. We evaluate the detection performance by implementing twelve machine learning methods and observe that feature selection significantly enhances detection speed with marginal accuracy improvement. Our discoveries highlight the capability of Q-SVM, imbalanced information, while asserting the viability of conventional AI approaches for non-time series information. These experiences help in choosing suitable discovery draws near, considering trade-offs between speed, accuracy, and cost. Our study highlights the promising role of Quantum Machine Learning (QML) in fraud detection, fostering future research in quantum computing applications

Description:

Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Extra Trees and Artificial Neural Networks (ANNs).