Explainable Generative ML–Driven Cloud-Native Risk Modeling with SAP HANA–Apache Integration for Data Safety

Abstract

Financial institutions increasingly require predictive models that are not only accurate but auditable, fair, and deployable at enterprise scale. This paper proposes an architectural and methodological framework for Cloud-Native Explainable Generative Machine Learning (XGen-ML) tailored to credit scoring and risk modeling, designed to run across SAP HANA (in-memory analytical platform) and the Apache ecosystem (Spark, Kafka, Hadoop) for large-scale ingestion, feature engineering, model training, synthetic data generation, and real-time scoring. The approach integrates three technical pillars: (1) generative modeling (GANs/VAEs/conditional generative models) to produce privacy-preserving synthetic tabular datasets for model training, stress-testing, and imbalance mitigation; (2) explainable AI (XAI) techniques — combining global explanation methods and instance-level interpretability (e.g., SHAP, LIME, rule extraction) — to provide audit trails for regulatory compliance and to expose drivers of decisions to business users; and (3) a cloud-native serving and governance layer using containerized microservices, model registries, feature stores, and stream processing to enable continuous training, monitoring, and model risk management. Generative models help address the scarcity and privacy constraints of sensitive credit datasets and can augment minority classes to reduce biased model performance, while XAI techniques ensure transparency, support human review, and enable contestability of decisions. The proposed system leverages SAP HANA’s in-memory SQL engine for high-performance analytical joins and feature computation, and the Apache stack (Spark for distributed ETL/ML, Kafka for streaming, Hadoop for archival) for scalable data processing and orchestration. We describe a reproducible methodological pipeline: (i) secure data ingestion and schema harmonization; (ii) synthetic data generation and augmentation using conditional tabular GANs; (iii) feature engineering and representation learning in Spark with push-down analytics to HANA where appropriate; (iv) model training using hybrid ensembles (tree-based + neural generative components) and causal feature selectors; (v) local and global explainability modules (SHAP for global attributions, LIME for local explanations, surrogate rules for regulatory reporting); (vi) deployment to a cloud-native inference service with runtime logging for model governance; and (vii) continuous monitoring for drift, fairness metrics, and performance degradation. We evaluate the framework qualitatively against regulatory requirements (e.g., explainability, auditability), operational constraints (latency, throughput), and model quality metrics (AUC, calibration, false-positive/negative cost asymmetries). Drawing on literature and applied case studies, we show that combining generative approaches with rigorous XAI and production-grade data platforms reduces the trade-off between performance and transparency while improving robustness to dataset shifts. The paper concludes with practical considerations for privacy (differential privacy extensions for generative models), governance (model registries, explainability documentation), limitations (synthetic data fidelity, worst-case fairness scenarios), and future extensions (causal generative models, privacy-preserving federated training). Key contributions are an end-to-end cloud-native architecture and a prescriptive methodology for integrating generative ML and XAI into credit and risk pipelines suitable for enterprise SAP HANA + Apache environments.