AI-Driven and Cloud-Native Compliance Frameworks as Strategic Tools for Combating Money Laundering and Cyber-Enabled Fraud in the United States

The accelerating scale and sophistication of money laundering and cyber-enabled fraud in the United States have outpaced the capabilities of traditional, rule-based compliance systems, which remain constrained by static thresholds, siloed data architectures, and limited adaptability to evolving threat vectors. This paper proposes and evaluates a novel AI-driven, cloud-native compliance framework designed to enhance the detection, prevention, and response capabilities of U.S. financial institutions and regulatory stakeholders. At the core of the proposed framework is a new hybrid algorithm, termed Adaptive Graph-Temporal Risk Inference (AGTRI), which integrates dynamic transaction-graph learning, temporal anomaly detection, and probabilistic risk scoring within a scalable cloud-native architecture. AGTRI combines graph neural networks (GNNs) for modeling complex financial relationships, temporal convolutional networks (TCNs) for capturing transaction-sequence dynamics, and Bayesian risk calibration layers to improve interpretability and regulatory auditability. The algorithm is benchmarked against widely deployed approaches, including rule-based AML engines, gradient-boosted decision trees, isolation forests, and long short-term memory (LSTM) models, using simulated and real-world anonymized transaction datasets representative of U.S. banking and payment systems. Performance comparisons demonstrate that AGTRI achieves statistically significant improvements in true positive detection rates (up to 27%), false-positive reduction (up to 34%), and latency under high transaction throughput, while maintaining explainability aligned with U.S. regulatory expectations. The paper presents comparative performance graphs, ROC and precision-recall analyses, cloud-scalability benchmarks, and cost-efficiency evaluations across monolithic and microservices-based deployments. Findings indicate that cloud-native orchestration using containerized microservices and event-driven processing enables near-real-time compliance monitoring without sacrificing model robustness or governance controls. By unifying advanced AI techniques with compliance-by-design cloud architectures, this research demonstrates a practical and scalable pathway for strengthening the United States’ defenses against money laundering and cyber-enabled fraud, while supporting regulatory transparency, operational efficiency, and national financial security.