Harnessing Quantum Computing and Generative AI for Next-Generation Credit Fraud Detection: Real-Time Anomaly Detection and Adversarial Risk Mitigation

Puneet Pahuja — Tue, 04 Nov 2025 00:00:00 +0300

The increasing complexity of financial crimes has revealed severe weaknesses in the conventional credit fraud detection systems. With the future and development of the digital payment ecosystem expanding rapidly around the world, there is an immediate necessity of smart, adaptive, and dynamic solutions, able to intercept the growing trends of fraud. The paper will apply a hybrid model based on combining both Quantum Computing and Generative Artificial Intelligence (GenAI) to transform credit fraud detection. Quantum Computing opens up initial brand-new levels of computational power and parallel processing with an understanding of mass data analysis within transactions. Meanwhile, GenAI advances fraud detection because GenAI can produce fake cases of fraud, simulate human behaviours, and increase model resilience. The combination of these technologies provides the basis for proactive detection of anomalies and dynamic risk evaluation. It is based on a conceptual architecture, which fuses quantum-enhanced learning models with adversarially trained GenAI systems. The framework helps detect anomalies in real-time, adapt to learn a greater diversity of fraud patterns, and mitigate risks adversarial through a convenient methodology based on federated learning and model training secrets. This is a multidisciplinary approach that is natural to both traditional systems based upon rules and establishes the way to a scalable, smart, and regulations-adherent fraud detection infrastructure. Our results indicate a transformative possibility of the integration of emerging technologies to construct next-generation security systems that can predict and defend credit fraud at a greater pace and accuracy.

Protecting Distributed Ledgers from Advanced Persistent Threats Using SVM-Based Blockchain Security

Kiruba Buri R, swaminathan K — Tue, 04 Nov 2025 00:00:00 +0300

This work focuses on multi-dimensional approach to incorporate the Support Vector Machine (SVM) models with Blockchain to secure distributed ledger against APTs. The classifying and high pattern recognition ability of SVM makes the proposed framework easily capture and neutralize malicious activities in the blockchain networks in realtime. The distribution of the blockchain technology and use of machine learning for predictive modeling guarantees a hard-coded countermeasure against new forms of cyber threats. As such, this work is centered on how these technologies can be integrated in harmony: attempting to enhance the accuracy of threat identification without compromising the functionality of the blockchain. This implementation shows the possibility of achieving strong, secure and scalable applications in different applications domains, and so make a way forward for upcoming decentralized cybersecurity solutions.

International Journal of communication and computer Technologies

Harnessing Quantum Computing and Generative AI for Next-Generation Credit Fraud Detection: Real-Time Anomaly Detection and Adversarial Risk Mitigation

Protecting Distributed Ledgers from Advanced Persistent Threats Using SVM-Based Blockchain Security