AI-Enhanced Cloud IAM for SAP HANA–Powered Credit Card Fraud Detection: Deep Learning, Data Integrity, and FDA-Compliant ERP Cloud Migration
DOI:
https://doi.org/10.15680/IJCTECE.2023.0602007Keywords:
AI-enhanced IAM, Cloud security, SAP HANA, Credit card fraud detection, Deep learning, Data integrity, FDA-compliant cloud migration, ERP integration, Real-time analytics, Anomaly detection, Fraud prevention, Machine learning, Secure access management, Financial cybersecurity, Scalable cloud frameworkAbstract
Credit card fraud continues to challenge financial institutions, necessitating advanced, secure, and compliant detection frameworks. This paper presents an AI-enhanced Cloud Identity and Access Management (IAM) framework for SAP HANA–powered credit card fraud detection that ensures data integrity and regulatory compliance, including FDA standards for cloud migration and ERP integration. The proposed system leverages deep learning algorithms to identify anomalous transaction patterns and potential fraud in real time. SAP HANA’s high-performance in-memory computing supports rapid data processing, while ERP integration ensures seamless interoperability across financial and operational modules. The cloud IAM layer enforces robust access controls, authentication protocols, and anomaly-based threat detection to secure sensitive financial and personal data. FDA-compliant migration strategies guarantee regulatory adherence and data traceability, enabling scalable, resilient, and secure deployment. Experimental evaluation demonstrates enhanced fraud detection accuracy, minimized false positives, and improved operational efficiency, offering a comprehensive solution for modern financial cybersecurity needs.References
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