Strengthening Financial Cybersecurity with SAP HANA: Deep Neural Networks and ERP-Integrated DevSecOps for MFA Credit Card Fraud Detection
DOI:
https://doi.org/10.15662/IJARCST.2023.0605007Keywords:
SAP HANA, Financial cybersecurity, Deep neural networks, Credit card fraud detection, Multi-factor authentication, ERP integration, DevSecOps, Real-time analytics, Fraud prevention, Machine learning, Behavioral anomaly detection, Secure fintech systems, SAP-driven security, Cyber threat detection, Financial data analyticsAbstract
Financial institutions face escalating cyber threats, particularly in the domain of credit card fraud, where attackers increasingly exploit digital payment infrastructures and authentication gaps. This paper presents an advanced cybersecurity framework that integrates SAP HANA’s in-memory processing capabilities with deep neural network models and ERP-aligned DevSecOps pipelines to enhance multi-factor authentication (MFA)–based fraud detection. The proposed architecture leverages SAP HANA’s real-time analytics to rapidly process transactional big data, enabling neural networks to identify subtle behavioral anomalies, high-risk patterns, and emerging fraud vectors. The DevSecOps integration ensures secure, automated deployment of fraud detection models across ERP environments, embedding continuous security testing, policy enforcement, and runtime monitoring into the development lifecycle. Additionally, MFA data is incorporated as a dynamic feature set to improve prediction accuracy and reduce false positives. Experimental results show significant improvements in detection speed, adaptability, and resilience against evolving cyber threats. This framework demonstrates a scalable, intelligent, and secure approach for modernizing financial cybersecurity through SAP-driven analytics, deep learning, and ERP-embedded DevSecOps practices.References
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