AI-ADAPTIVE POST-QUANTUM CRYPTOGRAPHY FOR SECURE AND FRAUD-RESILIENT DIGITAL PAYMENTS

The threat of quantum computing to classical cryptographic infrastructures is steadily increasing alongside more complex and multi-modal payment fraud posing a severe security gap within current financial infrastructures. Current payment stacks, which are based on TLS and have fixed classical cryptography and tabular fraud detectors, are inadequate to secure high volume digital transactions against both quantum decryption attacks as well as coordinated fraudulent behaviour. The purpose of the study was to create and test an AI-based framework that can optimally improve fraud detection and provide adaptive post-quantum cryptographic protection in real-time. To satisfy this goal, we developed an experimental system that merged a heterogeneous temporal graph neural network (HTGNN) to multimodal fraud analysis with a reinforcement-learning PQC orchestrator that can be used to choose NIST-standard ML-KEM and ML-DSA parameter sets based on transaction-level risk. Multimodal datasets (tabular features, device metadata, text fields, and relational graphs) were publicly available and converted into a fused format and tested in adversarial, latency, and throughput-controlled experiments. PQC-enabled OpenSSL/liboqs was used to implement the crypto layer to measure handshake performance in the real world. Findings indicate that the proposed model enhanced the AUPR of fraud-detection (baseline) of 0.35 to 0.58 and increased ROC-AUC to 0.966, which was better than the classical and hybrid systems. The adaptive PQC algorithm decreased the handshake latency to 68 ms, which was significantly lower than setups of PQC (90-120 ms). Robustness tests showed a smaller degradation in recall (−9) and a cipher-downgrade success rate of less than 1% which depicts a better resilience to the threat vectors. Altogether, the research results show that a combination of multimodal GNN-based fraud detection and AI-adaptive PQC is a viable and quantum-resilient security architecture of digital payment systems in the next generation.