Quantum Machine Learning for Pattern Recognition in Financial Fraud Detection

Abstract: Financial fraud detection is a persistent challenge due to the dynamic, adversarial nature of fraudulent activities. This study investigates the potential of Quantum Machine Learning (QML) algorithms for pattern recognition in financial datasets characterized by high dimensionality and imbalance. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) were trained and tested using the Qiskit platform on a representative fraud dataset. Performance metrics were evaluated against classical benchmarks and analyzed using statistical and regression models. The findings highlight that QML offers meaningful performance enhancements, particularly in F1 score and recall, suggesting its viability for future adoption in financial surveillance systems. Keywords: Quantum Machine Learning, Financial Fraud Detection, Pattern Recognition, Quantum Computing, QSVM, VQC, Predictive Analytics