Real-Time Fraud Detection and Cybersecurity in SAP HANA Using Machine Learning, Deep Learning, and DevSecOps Automation

Abstract

This paper presents an integrated approach for real-time fraud detection and cybersecurity in enterprise SAP HANA systems by combining machine learning (ML), deep learning (DL), and DevSecOps automation. Modern financial, retail, and enterprise ecosystems demand sub-second detection of anomalous transactions while maintaining system integrity, compliance, and traceability. SAP HANA’s in-memory processing and embedded analytics (PAL/APL) provide a low-latency platform suitable for operationalizing ML models in production. We propose a layered architecture: (1) real-time ingestion and feature engineering using change data capture (CDC) and streaming pipelines, (2) hybrid detection models — ensembles of gradient-boosted decision trees and DL sequence/graph models for transactional and relational anomalies, (3) explainability and risk scoring layers to reduce false positives, and (4) DevSecOps pipelines for automated model testing, secure model deployment, continuous monitoring, and incident response. Experimental and scenario-based results show that the hybrid approach reduces detection latency to near real-time and improves detection performance on imbalanced transactional datasets while DevSecOps automation reduces mean time to remediate vulnerabilities and model drift. We discuss advantages, limitations, and propose future work including graph neural networks for relational fraud and federated privacy-preserving training for cross-institution collaboration.