XAI-Enhanced Generative Models for Financial Risk: Cloud-Native Threat Detection and Secure SAP HANA Integration

Abstract

Financial institutions face an accelerating convergence of two pressures: (1) the need to detect increasingly subtle, adaptive threats (fraud, insider abuse, money-laundering, model drift) in real time across high-volume transaction streams, and (2) regulatory and stakeholder demands for model transparency and auditability. This paper presents a unified framework that combines explainable artificial intelligence (XAI) techniques and deep generative models (VGMs: VAEs, GANs and hybrid generative–discriminative architectures) to improve detection, interpretation, and traceability of financial risk signals, and describes how such a system can be deployed securely as cloud-native services integrated with SAP HANA for enterprise data management. The contribution is threefold. First, we propose a generative-enhanced anomaly detection pipeline that uses generative models to learn realistic transaction manifolds, detect deviations (anomalies and adversarial patterns), and synthesize counterfactuals for post-hoc explanation. Generative modeling supplies high-fidelity synthetic "normative" baselines that (a) reduce reliance on labeled anomaly data, (b) allow realistic stress-testing of downstream models, and (c) enable interpretable counterfactuals that explain why a transaction or sequence was flagged. Second, we embed XAI primitives—local explanation (LIME/SHAP style attribution), global surrogate models, concept activation vectors and counterfactual explanations—into the pipeline to provide layered explanations suitable for compliance teams, auditors, and model governance. These mechanisms expose feature influences, scenario-level drivers, and human-readable counterfactuals (what minimal changes would have made an alert non-anomalous), balancing fidelity and interpretability. Third, we describe system architecture and operational controls for deploying the pipeline as cloud-native microservices (Kubernetes, service mesh, CI/CD security gating) with real-time telemetry, model lifecycle management, and secure SAP HANA integration for canonical data storage, querying, and audit trails. 

Operationally, the approach reduces false positives by modeling complex normal behaviors and thus clarifies anomalies that are truly suspicious. It also strengthens defenses against adversarial manipulation by enabling generative replay and adversarial training, and by surfacing the model’s sensitivity to plausible data perturbations via counterfactuals. From a governance perspective, embedding XAI helps satisfy regulatory transparency requirements (credit underwriting, AML audits) by producing consistent, versioned explanation artifacts stored alongside models and transaction events in SAP HANA. The cloud-native design enables scalability, rapid model updates, and secure, observable telemetry—while SAP HANA provides high-performance in-database analytics and a hardened audit/logging substrate for evidentiary trails. 

We validate the framework in two case studies: (1) simulated high-frequency payment streams with injected sophisticated fraud campaigns, and (2) credit scoring drift detection on a longitudinal loan portfolio. Results show notable improvements in detection precision (reducing false alarms by 18–32% depending on the scenario) and explanation utility (measured by investigator time-to-resolution and human-evaluated explanation usefulness). We highlight operational trade-offs—compute/latency costs from generative sampling, the complexity of reconciling model explanations with business logic, and potential privacy concerns when generating synthetic data—and propose mitigations: privacy-preserving generative training (differential privacy regularization), selective sampling for low-latency paths, and governance controls embedded in SAP HANA. Finally, we discuss research directions including continual learning under concept drift, standardized explainability SLAs, and automated regulatory reporting pipelines. (SpringerLink)