Enterprise Fraud Prevention with Causal Trace Miner–Enhanced Deep Learning, Cloud-Native DevSecOps Pipelines, and SAP HANA ERP Security Analytics
DOI:
https://doi.org/10.15662/IJARCST.2021.0403002Keywords:
Causal Trace Miner, Enterprise Fraud Prevention, Deep Learning, SAP HANA ERP, Cloud Security, DevSecOps Pipelines, Neural Networks, Real-Time Analytics, LSTM, Autoencoders, Process Mining, Financial Security, Identity Threat Detection, Cybersecurity, CI/CD Automation, Anomaly Detection, Transaction Monitoring, ERP Intelligence, Cloud-Native Security, Event CausalityAbstract
Enterprise fraud is escalating in complexity due to the rise of cloud-native computing, interconnected ERP systems, multi-channel digital transactions, and the rapid scaling of identity-driven workflows. Conventional fraud detection approaches relying on deterministic rules and isolated analytics are insufficient to identify hidden, multi-stage behaviors that span transactional, identity, cloud, and ERP environments. This research presents an integrated enterprise fraud prevention architecture that leverages Causal Trace Miner analytics, deep neural networks, SAP HANA ERP security analytics, and cloud-native DevSecOps pipelines. The proposed model reconstructs cross-system causality to expose fraudulent behavioral paths, while deep learning models—including LSTM, CNN-LSTM, and Autoencoders—enhance detection accuracy by capturing temporal and latent anomaly patterns within large-scale datasets. SAP HANA’s in-memory platform supports real-time processing of ERP workflows, user roles, and financial transactions. Cloud-native DevSecOps pipelines automate security scanning, CI/CD deployments, policy enforcement, and continuous monitoring of AI models to ensure resilience and adaptability against evolving cyber-fraud threats. Experimental evaluation indicates significant improvements in anomaly detection accuracy, reduced false positives, improved causal interpretability, and enhanced system resilience. The proposed framework delivers a comprehensive, scalable, and explainable enterprise fraud defense system suitable for modern financial, ERP, and cloud environments.References
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