Causal Trace Miner–Powered AI Fraud Detection Using Deep Learning with DevSecOps and SAP HANA ERP Integration
DOI:
https://doi.org/10.15662/IJRAI.2021.0403004Keywords:
Causal Trace Miner, Deep Learning, Fraud Detection, DevSecOps, SAP HANA ERP Analytics, Enterprise Security, Autoencoders, LSTM, Cloud Security, Process Mining, Anomaly Detection, AI-Driven Fraud PreventionAbstract
Enterprise fraud has evolved into a sophisticated and multidimensional threat that spans financial operations, identity management, business processes, and cloud-native infrastructures. Traditional rule-based or statistical models fail to capture temporal dependencies, causal relationships, and high-dimensional patterns embedded in enterprise activity logs. This paper introduces a Causal Trace Miner–powered AI fraud detection framework that leverages deep learning, DevSecOps automation, and SAP HANA ERP integration to address modern fraud detection challenges. Causal Trace Miner (CTM) reconstructs event dependencies across business workflows, enabling early identification of process deviations and multi-step fraudulent sequences. Deep learning models—including LSTM, Autoencoders, and Attention mechanisms—enhance detection accuracy by learning temporal patterns and behavioral anomalies. The adoption of cloud-native DevSecOps pipelines ensures continuous security validation, automated deployment, and resilience against adversarial manipulation. SAP HANA ERP integration provides real-time analytics, fraud insights, and embedded operational intelligence. Extensive evaluation demonstrates substantial improvements in fraud detection accuracy, reduction in false positives, and enhanced process transparency. This research contributes a unified, scalable, and enterprise-ready fraud detection architecture that addresses emerging threats across digital ecosystems, ERP modules, and cloud environments.References
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