arXiv:2605.21164v1 Announce Type: new Abstract: Credit card fraud detection is fundamentally challenged by extreme class imbalance, where fraudulent transactions are rare yet operationally critical. This imbalance often biases supervised learners toward the legitimate class, leading to high overall accuracy but weaker fraud-class recall and F1-score. This paper introduces Q-SYNTH, a hybrid classical--quantum generative adversarial framework in which a parameterized quantum circuit serves as the generator and a classical neural network serves as the discriminator. Q-SYNTH is designed for minori
The increasing sophistication of financial fraud necessitates advanced detection methods, alongside the growing maturity of quantum computing research allowing for hybrid applications.
This development indicates a promising direction for enhancing fraud detection capabilities in critical financial systems, impacting the security and stability of transactions.
Traditional fraud detection, which often struggles with imbalanced datasets, can be augmented and potentially surpassed by hybrid quantum-classical approaches, leading to improved accuracy for rare events.
- · Financial institutions
- · Cybersecurity sector
- · Quantum computing developers
- · Fraudsters
- · Legacy fraud detection systems
Improved fraud detection rates reduce financial losses for institutions and consumers.
Increased trust in digital financial transactions could accelerate the adoption of new payment methods.
The success of quantum-classical hybrids in finance could spur similar innovations in other data-imbalanced critical applications.
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Read at arXiv cs.LG