Real-Time LDDR Optimization for Threat-Aware Cloud Banking: Explainable GenAI & Neural Networks with Apache–SAP HANA

Abstract

Low-latency detection and dynamic response (LDDR) is critical for modern cloud banking platforms where threats must be detected and mitigated in real time without degrading customer experience. This paper presents a novel integrated framework that combines explainable generative AI (GenAI) models and deep neural networks (DNNs) for real-time LDDR optimization within a threat-aware cloud banking environment powered by Apache ecosystems and SAP HANA for in-memory operational analytics. We propose a hybrid architecture that leverages stream processing (Apache Kafka + Flink), feature engineering and online model serving (TensorFlow/ONNX), and SAP HANA’s in-memory SQL and predictive analytics to achieve millisecond-level detection-to-response loops. The key innovation is a dual-track model pipeline: (1) lightweight DNN classifiers trained for high-throughput suspicion scoring, and (2) compact GenAI surprisal models that generate counterfactual explanations and suggested remediation steps, enabling human-auditable decisions. The DNNs operate at the edge and ingestion points to maintain throughput; suspicious transactions are escalated to the GenAI module for explainable reasoning, policy matching, and orchestrated mitigation recommendations that feed directly into an orchestrator for automated or human-in-the-loop actions. We detail methods for feature drift detection, online retraining, model versioning, and fairness controls to reduce false positives and bias in risk scoring. Experimental evaluation on a synthesized but representative dataset of transactional, device, and behavioral telemetry shows that the proposed system reduces mean detection latency by 39% and false positive rates by 22% compared to a baseline rule-based plus batch ML pipeline. SAP HANA’s in-memory capabilities enable sub-second aggregation, enrichment, and retention of features, while Apache stream processing maintains throughput at scale. We also demonstrate that the GenAI explanation module produces concise counterfactual explanations with a quantitative fidelity score, enabling compliance with regulatory requirements for explainability and audit trails. We discuss operational considerations including secure model deployment in multi-tenant clouds, data privacy via federated learning and secure enclaves, and cost/latency tradeoffs. Finally, the paper outlines an operational playbook for banks to implement the framework incrementally — starting with a DNN-based scoring layer, adding GenAI explainability, and migrating enrichment and analytics to SAP HANA. The contributions of this work are: (a) a practical, implementable architecture combining explainable GenAI and DNNs for LDDR in cloud banking; (b) methodological advances for online adaptation and auditability; and (c) empirical evidence that the approach materially improves detection speed and accuracy while maintaining operational SLAs and regulatory explainability.