  This paper 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 Quantum Support Vector Machine's feature map characteristics. The results
are compared using fraud specific key performance indicators: Accuracy, Recall,
and False Positive Rate, extracted from analyses based on human expertise (rule
decisions), classical machine learning algorithms (Random Forest, 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 which led to an improved
accuracy of the mixed quantum-classical method for fraud detection, on a
drastically reduced data set to fit current state of Quantum Hardware.
