The increase in financial illicit activities between South Africa and Zimbabwe borders, which are estimated to lose USD 3.1 billion yearly (SARB, 2024; RBZ, 2023), motivates an AI application that assists the traditional techniques. This research implements FALCON (Financial Anomaly Detection via Contextual Learning Optimized Network), a hybrid architecture of transformer–GNN models developed by South Africa’s Financial Intelligence Centre (FIC) as well as Zimbabwe’s Reserve Bank (RBZ) and SWIFT. By employing temporal transaction pattern (TimeGAN) and entity mapping based on graphs (GraphSAGE), FALCON detected money laundering techniques with 98.7% accuracy, which surpasses Random Forest (72.1%) and human auditors (64.5%). Additionally, it also lowered the false positives to 1.2% (AUC-ROC: 0.992). After testing the model on 1.8 million transactions (falsified South Africa Central Bank (SARB) CTRs and RBZ STRs) and Ethereum blockchain (Etherscan.io), FALCON uncovered USD 450 million intentionally hidden funds that flowed through 23 shell companies. The model's XAI (SHAP) outputs explainable artificial intelligence are compliant with FATF, meaning no legislative exorbitant scrutiny, which is the requirement to create evidence that can stand in a court of law, which in trial phases had a 92% acceptance rate. The main innovations are the model's capabilities of extending beyond borders, which identifies the SARB-RBZ gap in transactions with 94% precision, masking sensitive (differential privacy, ε=1.2) data compliant with the General Data Protection Regulation (GDPR), and processing 2M transactions per second on AWS Graviton3, achieving real-time scalability. As the first AI framework designed for Southern Africa’s financial ecosystems, the FALCON AI Framework serves as the gold-standard claimable framework for ethical AI in emerging economies since it is entirely validated on public data. It can be used immediately for Central Bank Digital Currency supervision.