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

Abstract

In the era of digital banking, financial institutions increasingly deploy artificial intelligence (AI) to enhance real‑time risk management, including credit underwriting and threat detection. However, deploying highly capable but opaque models raises regulatory, ethical, and security concerns. This paper proposes a novel architecture that combines generative AI, explainable AI (XAI), and secure real‑time processing on an Apache–SAP HANA cloud platform, tailored for an AI‑first banking environment. Our system uses generative models (e.g., variational autoencoders or generative adversarial networks) to synthesize enriched financial data, augmenting scarce or sensitive customer datasets while preserving privacy. These synthetic data enhance credit‑risk modeling and threat‑risk detection in scenarios where real data are limited. The risk models themselves are based on powerful machine‑learning models (e.g., gradient boosting, deep networks) but are wrapped in explainability mechanisms such as SHAP and LIME, enabling both local and global interpretability for credit officers, auditors, and regulators. All components are integrated into a secure, low-latency real‑time architecture on Apache HANA, leveraging in‑memory storage, columnar processing, and built-in encryption and role-based access controls on SAP HANA Cloud. The system supports real-time inference for credit decisions and threat scoring, with dynamic explainability feedback and logging for auditability. We validate the design via a proof-of-concept implementation using synthetic-bank datasets and simulated threat events, demonstrating that (1) generative AI augmentation improves predictive performance, (2) XAI techniques provide meaningful, actionable explanations without degrading model accuracy, and (3) the HANA-based architecture ensures rapid, secure real-time operation. The proposed architecture balances accuracy, interpretability, performance, and security, paving the way for more transparent, trustworthy, and scalable risk modeling in next-generation banking.