Quantum Classical Synergy for Fraud Detection

Abstract: Detecting fraudulent financial transactions is inherently challenging due to extreme class imbalance, complex high- dimensional feature spaces, and the adaptive strategies employed by fraudsters. To overcome these limitations, this work presents a hybrid quantum–classical fraud detection framework that strategically combines conventional machine learning models with quantum computing techniques to enhance predictive reliability. Rich feature representations are constructed by integrating transactional, behavioral, and graph-based attributes to capture both local and relational fraud characteristics. A Quantum Autoencoder (QAE) is utilized to perform efficient dimensionality reduction, enabling compact feature encoding while retaining essential discriminatory patterns. The reduced feature set is subsequently processed using Cat-Boost, alongside Quantum Neural Networks (QNNs) or Variation Quantum Classifiers (VQCs) to leverage complementary learning capabilities. Model outputs are aggregated through a stacking-based meta- classification strategy, resulting in improved generalization and decision stability. Performance evaluation is carried out using fraud-oriented metrics, including recall, precision, accuracy, and false positive rate. Experimental analysis indicates that the proposed hybrid approach consistently surpasses standalone classical and quantum models, achieving superior fraud recall with notably fewer false alarms. These results demonstrate the robustness, scalability, and practical viability of quantum–classical integration, positioning the proposed framework as a promising foundation for next- generation financial fraud detection systems.