Mixed Quantum–Classical Method for Fraud Detection With Quantum Feature Selection

Abstract

This article presents a first end-to-end application of a quantum support vector machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art quantum machine-learning algorithms with respect to the classical approach. A new method to search for best features is explored using the QSVM's feature map characteristics. The results are compared using fraud-specific key performance indicators, i.e., accuracy, recall, and false positive rate, extracted from analyses based on human expertise (such as rule decisions), classical machine-learning algorithms (such as random forest and XGBoost), and quantum-based machine-learning algorithms using QSVM. In addition, a hybrid classical–quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space that led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced dataset to fit current state of quantum hardware.

Keywords

• Feature selection
• fraud detection
• quantum
• quantum kernel alignment
• quantum support vector machine (QSVM)