Real-Time Cloud Threat Intelligence and Machine Learning–Enhanced Explainable Generative AI for Credit and Risk Modeling using a Secure Apache–SAP HANA Architecture

Abstract

Real-time credit risk assessment is critical for financial institutions to prevent defaults, manage capital, and comply with regulatory norms. In this study, we propose a secure, cloud-native architecture that integrates real-time cloud threat intelligence with machine learning–enhanced, explainable generative AI, all built on an Apache + SAP HANA in-memory secure data backbone. The system ingests streaming transactional and behavioral data from loan applicants and existing borrowers, enriches it with threat intelligence to detect anomalous or malicious activity (e.g., fraud, identity theft), and uses a hybrid generative AI–ML model to produce synthetic scenarios, explainable risk forecasts, and counterfactual analyses. The generative component allows scenario augmentation (e.g., stress testing), while explainability modules like SHAP provide transparency. The SAP HANA architecture supports low-latency, high-throughput analytics, and its security capabilities (access control, encryption, audit logging) help protect sensitive financial data. We evaluate our framework using simulated data and real-world credit datasets, measuring predictive performance (AUC, F1), explainability, and threat-detection efficacy. Results show that integrating threat intelligence improves early fraud detection, the generative AI layer enhances the richness of risk scenarios, and explainability helps compliance. We discuss the advantages, limitations, and deployment challenges, and outline future work for integrating advanced cloud-native security controls and federated learning.