Explainable Generative AI–Enhanced Credit and Threat Risk Modeling in AI-First Banking: A Secure Apache–SAP HANA Real-Time Cloud Architecture

Abstract

Financial institutions increasingly require advanced, intelligent, and interpretable AI systems for critical domains such as credit risk scoring, fraud and threat analytics, and automated banking operations. Yet, deploying generative AI in real time, securely, and with built-in explainability remains a major challenge. In this paper, we propose a secure real‑time Apache–SAP HANA cloud framework that integrates generative AI models with explainable artificial intelligence (XAI), leveraging the hybrid transactional/analytical processing (HTAP) capability of SAP HANA. Our architecture supports real-time data ingestion via Apache streaming components (e.g., Kafka), low-latency in-memory analytical processing in SAP HANA Cloud, and secure model serving with built-in counterfactual and attribution-based explanations. We detail three use‑cases: (1) credit risk assessment, where the generative AI (e.g., GANs or variational autoencoders) augments sparse or imbalanced borrower data; (2) threat analytics, where generative models simulate attack scenarios and XAI helps explain anomalous risk; (3) AI-first banking operations, such as automated decisioning for loan approvals or credit-line management. We also design a security layer that ensures data privacy, model integrity, and access control using role-based encryption and secure enclaves. Our experimental evaluation, using synthetic and real banking datasets, demonstrates that the proposed framework reduces latency compared to batch-based scoring systems, improves predictive accuracy (especially for rare-event defaults), and yields human-readable explanations via SHAP and counterfactuals. We discuss trade‑offs (e.g., model complexity vs. interpretability, security overhead) and provide insights into how financial institutions can deploy such systems in practice. Finally, we outline future directions for regulatory compliance, federated learning, and proactive AI risk monitoring in banking.