Explainable Generative AI-Driven Bank Credit Risk Modeling: Secure Apache–SAP HANA Cloud Integration for Threat-Focused Data Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806812Keywords:
Explainable Generative AI, Bank Credit Risk Modeling, Bank Credit Risk ModelingSAP HANA Cloud, Apache Security Tools, Threat-Aware Analytics, Secure Data Pipelines, Financial Risk IntelligenceAbstract
The growing complexity of financial risks and the rapid evolution of cyber threats demand advanced analytical frameworks capable of supporting secure, transparent, and real-time decision-making in the banking sector. This paper presents an Explainable Generative AI–driven framework for credit risk modeling that integrates Apache security tools with SAP HANA’s in-memory cloud architecture to provide high-performance, threat-aware data analytics. The proposed system enhances credit scoring accuracy through generative modeling while ensuring regulatory compliance by incorporating explainability, traceability, and bias detection mechanisms. Additionally, the architecture leverages Apache-based threat monitoring and secure data pipelines to mitigate vulnerabilities across the cloud infrastructure. Experimental evaluation demonstrates improved predictive performance, reduced false positives in risk classification, and strengthened data security in high-volume banking environments. The framework provides a scalable and interpretable foundation for next-generation autonomous risk management in cloud-enabled financial systems.
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