Real-Time Cloud Threat Intelligence for AI-First Banking: An Explainable Generative AI Framework for Credit and Risk Modeling using Apache and SAP HANA

Abstract

In an increasingly digital banking environment, the convergence of real‑time cloud threat intelligence and AI‑driven credit risk modeling offers transformative potential. This work proposes a novel, explainable generative AI framework tailored to an AI‑first banking architecture, leveraging Apache distributed computing and SAP HANA in‑memory database technologies. The framework ingests continuous cyber threat intelligence feeds from the cloud—such as Indicators of Compromise (IoCs), threat-actor behaviors, and adversarial tactics—and integrates them into credit and risk scoring pipelines. A generative model (e.g., a conditional Generative Adversarial Network or transformer‑based LLM) synthesizes threat-informed features, enabling proactive adaptations to credit models in response to emerging cyber risk. Explainability is achieved via post-hoc explanation methods (e.g., SHAP, LIME) and prompt-based natural-language justification modules, ensuring transparency and auditability for regulatory compliance.

 We evaluate our framework on a simulated banking dataset combined with synthetic threat-stream data. Experiments show that threat-aware generative augmentation improves credit default prediction performance (e.g., AUC) compared to baseline models, while maintaining interpretability. The use of Apache Spark ensures real-time feature engineering and streaming, whereas SAP HANA’s in‑memory capabilities facilitate low-latency inference and decisioning.

 The contribution of this research lies in bridging cyber threat intelligence with financial risk modeling, providing a real‑time, explainable, scalable system. We discuss advantages (e.g., proactive risk mitigation, improved model robustness) and potential drawbacks (e.g., model complexity, data integration challenges). Finally, we outline future directions including deployment in production banking environments, regulatory validation, adversarial robustness, and continuous feedback loops.