AI and LLM-Powered Declarative Security for Credit Card Fraud Detection: Deep Neural Networks, Cloud Threat Mitigation, DevSecOps CI/CD, and SAP HANA ERP Integration
DOI:
https://doi.org/10.15680/IJCTECE.2020.0405006Keywords:
Credit Card Fraud Detection, Deep Neural Networks, LLM Reasoning, Declarative Security, DevSecOps, CI/CD, SAP HANA, Cloud Threat Mitigation, Machine Learning Security, ERP Analytics, AI Governance, Anomaly Detection, In-Memory Databases, Zero-Trust Architecture, Automated Security PipelinesAbstract
The accelerated digitization of financial services has increased exposure to fraud, requiring next-generation intelligent security systems that combine advanced analytics, cloud security, and automated reasoning. This research presents an integrated framework for credit card fraud detection using Deep Neural Networks (DNNs), Large Language Models (LLMs), cloud-native threat mitigation strategies, DevSecOps-driven CI/CD pipelines, and SAP HANA ERP analytics. The framework enhances fraud detection accuracy, introduces declarative security policies enforced through LLM reasoning, and strengthens enterprise resilience through automated DevSecOps. A multilayer architecture processes high-velocity financial transactions using DNN-based anomaly detection, while SAP HANA enables real-time in-memory analytics and ERP contextualization. LLMs perform natural-language-based security interpretation, root-cause reasoning, and policy verification, reducing operational friction for security analysts. Cloud-native security mechanisms provide continuous monitoring, automated scanning, and secure model deployment. Experiments demonstrate improved precision, faster detection times, and reduced false positives compared to traditional systems. This study contributes a unified, intelligent, explainable, and scalable fraud-prevention ecosystem suitable for modern financial enterprises and digital commerce infrastructures.
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