Hardware-Enforced Confidential Computing and Homomorphic Encryption for Cross-Bank Financial Fraud AI

Abstract

Abstract: Sophisticated financial crime syndicates orchestrate synthetic identity fraud, layering, and Authorized Push Payment (APP) laundering across dozens of correspondent banks and national jurisdictions within minutes. Single-institution machine learning models fail to detect these cross-bank topological patterns because each bank observes only an isolated local subgraph of the global transaction flow. However, statutory banking secrecy laws, GDPR Article 6/9 data protection mandates, and competitive confidentiality prohibit financial institutions from pooling cleartext account histories or counterparty graphs in a shared public cloud data lake. Conversely, executing deep Graph Neural Networks (GNNs) or gradient-boosted decision trees purely inside Fully Homomorphic Encryption (FHE) schemes incurs four to five orders of magnitude computational slowdown due to non-polynomial activation bootstrapping, rendering real-time payment screening impossible. This paper proposes a Hybrid Cryptographic-Enclave Cross-Bank Fraud AI architecture that combines Cheon-Kim-Kim-Song (CKKS) homomorphic encryption with hardware-attested Confidential Virtual Machines (AMD SEV-SNP and Intel TDX) coupled to NVIDIA H100 Confidential GPUs via encrypted PCIe/CXL integrity buses. Member banks encrypt local velocity vectors and counterparty risk embeddings under a threshold-shared CKKS public key so that untrusted cloud ingestion brokers perform homomorphic cross-bank feature aggregation in ciphertext without enclave memory exposure. The aggregated ciphertexts then enter a remotely attested AMD SEV-SNP + H100 Trusted Execution Environment (TEE) that performs threshold decryption, executes non-linear GNN and XGBoost scoring in hardware-encrypted memory, and re-encrypts risk alerts. Evaluated on a cross-border interbank anomaly detection case study modeled on SWIFT and JPMorgan Chase correspondent banking networks across 12 participating institutions, the hybrid architecture boosts cross-bank laundering recall from 58.2% (single-bank baseline) to 93.6%, reduces end-to-end encrypted scoring latency from 14,800 ms (pure FHE) to 28.5 ms, and guarantees zero cleartext exposure to cloud hypervisors or rival banks. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Cloud Security, Confidential Enclaves, and Financial AI. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.