Explainable Generative AI for Cloud-Native Cyber Defense: Apache-Driven Real-Time Threat Detection, Credit Risk Analytics, and Fraud Prevention
DOI:
https://doi.org/10.15662/IJARCST.2023.0604004Keywords:
Real-time analytics, redit risk modelling, Generative AI, Explainable AI (XAI), Apache Kafka / Flink, In-memory computing, SAP HANA, Cloud-native banking, Threat-aware AI, Risk governanceAbstract
In the rapidly evolving banking sector, real‑time credit risk and threat monitoring have become mission-critical as financial institutions adopt AI‑first architectures for credit decisioning and risk management. This paper proposes a novel threat-aware cloud analytics framework, combining Apache stream‑processing technologies with SAP HANA’s in‑memory computing, to deploy explainable generative AI models for credit and risk modeling. The architecture ingests streaming transactional, behavioral, and contextual data via Apache Kafka and Apache Flink, processes it in real time, and leverages generative models—augmented by retrieval mechanisms (e.g., Retrieval-Augmented Generation)—to simulate “what-if” credit scenarios, stress tests, and adversarial threat patterns. These generative outputs are rooted in real data and made interpretable using explainability techniques (e.g., SHAP, LIME), ensuring regulatory transparency and auditability.
We evaluate the system on simulated and real banking datasets, demonstrating improvements in risk prediction, response latency, and anomaly detection compared to traditional batch credit-scoring models. The in-memory capabilities of SAP HANA support ultra-low latency analytics, enabling intraday risk recalculation and near-instant feedback to decision engines. Our results show that the generative AI model augmented with real‑time streaming yields higher prediction robustness, better calibration, and improved detection of unusual or adversarial risk patterns. Moreover, the explainability layer fosters trust among stakeholders, helping compliance teams, credit analysts, and regulators understand model decisions.
This work contributes to the literature by bridging streaming analytics, generative AI, and explainable credit risk models in a unified, cloud-native architecture. It demonstrates how banks can operationalize AI‑first risk strategies while maintaining governance, performance, and compliance. Future directions include scaling to multi-tenant cloud deployments, incorporating more threat vector data, and refining generative models for adversarial risk simulation.
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