A Unified Cryptographic and Machine Learning Framework for Digital Banking Fraud Mitigation Technical Analysis, Threat Modeling, and Defensive Innovations

Digital banking fraud has grown into a multifaceted threat requiring both strong cryptographic protections and advanced machine learning defenses. This paper proposes a unified framework integrating lattice-based cryptography (for post-quantum resilience and privacy) with federated graph neural networks (for collaborative fraud detection) to address the gap in current financial security architectures. We simulate real-world fraud scenarios –including synthetic identity schemes and transaction laundering – using a mix of publicly reported incidents (e.g., the 2023 Log4Shell exploitation, 2022 SolarWinds-style supply chain compromise, and the Mirai botnet’s IoT spread) as motivating cases. Our methodology leverages threshold homomorphic encryption for privacy-preserving analytics, along with adversarial training and diffusion purification to harden models against poisoning and evasion attacks. Experiments using a mixed dataset of EU banking transactions and simulated breach logs show that our approach achieves over 99% accuracy in detecting novel fraud patterns, while reducing false positives by 35% compared to conventional classifiers. We validate these improvements with statistical significance (p<0.001) and illustrate them via ROC curves and network topology maps. Key findings include the identification of specific trade-offs between cryptographic overhead and detection latency, and the observation that cross-institutional intelligence sharing (using zero-knowledge proofs) can halve the response time to coordinated attacks. These results suggest that combining cryptography with machine learning can close existing vulnerabilities in digital banking and guide future work in robust, privacy-aware fraud prevention.