The growing use of online banking and digital payment systems has increased the risk of fraudulent activities. For this reason, identifying suspicious transactions is an essential task that should continuously evolve with technological advances and new forms of criminal activity. In recent years, fraud detection has been commonly implemented by exploiting Machine Learning (ML) models, which are able to detect irregular patterns and identify fraudulent transactions. However, classical ML performance is not always satisfactory, especially in strongly unbalanced classification tasks like fraud identification. To overcome these limits, Quantum Machine Learning (QML) has recently gained researchers’ interest. Indeed, these systems, working intrinsically in a higher-dimensional computation space thanks to superposition and entanglement, can implement more complex models than the classical counterparts, potentially providing a significant advantage in complex pattern recognition.