AI-Augmented Fraud Detection in Cloud Platforms: GRA-Based Risk Ranking with Cybersecurity and Threat Prevention for SAP HANA Healthcare ERP
DOI:
https://doi.org/10.15662/IJARCST.2024.0705011Keywords:
AI-augmented analytics, Grey Relational Analysis, fraud detection, risk ranking, cloud security, SAP HANA, healthcare ERP, threat prevention, anomaly detection, cybersecurity intelligenceAbstract
The increasing digitalization of healthcare enterprise systems and cloud-based ERP environments has amplified the need for advanced, risk-aware fraud detection mechanisms. This study presents an AI-augmented fraud detection framework that integrates Grey Relational Analysis (GRA) for risk ranking with cloud-native machine learning, cybersecurity controls, and SAP HANA–aligned analytics. The proposed model leverages multi-source healthcare ERP data—including financial transactions, patient billing records, access logs, and operational workflows—to detect anomalies and classify fraud risk with higher precision. GRA is employed to compute relational grades and generate a prioritized risk score, enabling security teams to focus on high-impact threats. The AI pipeline incorporates supervised and unsupervised models for behavioral profiling, anomaly detection, and entity risk assessment. Cloud security mechanisms such as identity governance, zero-trust access, encryption, and continuous threat monitoring strengthen the reliability and resilience of the system. The integrated threat prevention module uses adaptive rule engines and autonomous alerting to mitigate attacks before they escalate. Experimental evaluations within simulated SAP HANA healthcare ERP environments demonstrate improved detection accuracy, enhanced interpretability, and reduced false positives. The framework offers a scalable, explainable, and secure approach to combating fraud and cyber threats in modern healthcare cloud infrastructures.
References
1.Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2017). SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark. arXiv preprint arXiv:1709.08920. arXiv+1
2.Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
3.Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). Balanced aware firefly optimization based cost-effective privacy preserving approach of intermediate data sets over cloud computing.
4.Kumar, R. K. (2023). Cloud-integrated AI framework for transaction-aware decision optimization in agile healthcare project management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(1), 6347–6355. https://doi.org/10.15680/IJCTECE.2023.0601004
5.Pasumarthi, A., & Joyce, S. SABRIX FOR SAP: A COMPARATIVE ANALYSIS OF ITS FEATURES AND BENEFITS.
6. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–
7.Islam, M. S., Pourmajidi, W., Zhang, L., Steinbacher, J., Erwin, T., & Miranskyy, A. (2020). Anomaly Detection in a Large-scale Cloud Platform. arXiv preprint arXiv:2010.10966. arXiv
8.Komakula, S., & Jagadeeshwar, M. (2020). Artificial neural networks based techniques for anomaly detection in Apache Spark. Cluster Computing, 23, 1345–1360. SpringerLink
9.Hardial Singh, “Securing High-Stakes DigitalTransactions: A Comprehensive Study on Cybersecurity and Data Privacy in Financial Institutions”, Science, Technology and Development, Volume XII Issue X OCTOBER 2023.
10. Panguluri, L. D., Mohammed, S. B., & Pichaimani, T. (2023). Synthetic Test Data Generation Using Generative AI in Healthcare Applications: Addressing Compliance and Security Challenges. Cybersecurity and Network Defense Research, 3(2), 280-319.
11.Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7941-7950.
12. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
13.Haggag, M., et al. (2021). Fraud Detection Techniques in the Big Data Era. Proceedings of the International Conference on Big Data, Modelling and Machine Learning (BML’21). SciTePress
14. Althati, C., Rambabu, V. P., & Devan, M. (2023). Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection. Journal of Computational Intelligence and Robotics, 3(1), 123-162.
15.Musham, N. K., & Aiswarya, R. S. (2019). Leveraging Artificial Intelligence for Fraud Detection and Risk Management in Cloud-Based E Commerce Platforms. International Journal of Engineering Technology Research & Management, 3(10). IJETRM
16.Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
17. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014
18. Mishra, S. (2018). Scaling rule based anomaly and fraud detection and business process monitoring through Apache Flink. Australian Journal of Machine Learning Research & Applications. sydneyacademics.com
19.. Zubair, K. M., Akash, T. R., & Chowdhury, S. A. (2023). Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense. Frontiers in Computer Science and Artificial Intelligence, 2(2), 26-51.
20. Sridhar Reddy Kakulavaram, Praveen Kumar Kanumarlapudi, Sudhakara Reddy Peram. (2024). Performance Metrics and Defect Rate Prediction Using Gaussian Process Regression and Multilayer Perceptron. International Journal of Information Technology and Management Information Systems (IJITMIS), 15(1), 37-53.
21. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
22. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
23.Esmaeilzadeh, S., Salajegheh, N., Ziai, A., & Boote, J. (2022). Abuse and Fraud Detection in Streaming Services Using Heuristic Aware Machine Learning. arXiv preprint arXiv:2203.02124. arXiv
24.Dal Pozzolo, A., Boracchi, G., Caelen, O., & al. (2015). Ensemble Learning for Data Stream Analysis. Proceedings of IJCNN 2015. (as referenced in concept-drift literature) Wikipedia
25. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
26.Alippi, C., & Polikar, R. (2014). Guest Editorial: Learning in Nonstationary and Evolving Environments. IEEE Transactions on Neural Networks and Learning Systems. (not exact page but foundational to concept drift) Wikipedia
27.Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). A Cost-Effective Privacy Preserving Using Anonymization Based Hybrid Bat Algorithm With Simulated Annealing Approach For Intermediate Data Sets Over Cloud Computing. International Journal of Computational Research and Development, 2(2), 173-181.
28. Vijayaboopathy, V., Rao, S. B. S., & Surampudi, Y. (2023). Strategic Modernization of Regional Health Plan Data Platforms Using Databricks and Advanced Analytics Algorithms. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 172-208.
29.. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.
30.Kato, R., & Klyuev, V. (2017). Anomaly based intrusion detection using Apache Hadoop and Spark. (Details from literature cited in H. Gandhi & P. Sharma 2025 survey) SciTePress
31. Sivaraju, P. S. (2023). Thin client and service proxy architectures for X systems in distributed operations. International Journal of Advanced Research in Computer Science & Technology, 6(6), 9510–9515.
32. Thangavelu, K., Kota, R. K., & Mohammed, A. S. (2022). Self-Serve Analytics: Enabling Business Users with AI-Driven Insights. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2, 73-112.
33. Mani, R. (2022). Enhancing SAP HANA Resilience and Performance on RHEL using Pacemaker: A Strategic Approach to Migration Optimization and Dual-Function Infrastructure Design. International Journal of Computer Technology and Electronics Communication, 5(6), 6061-6074.
34. Navandar, P. (2023). The Impact of Artificial Intelligence on Retail Cybersecurity: Driving Transformation in the Industry. Journal of Scientific and Engineering Research, 10(11), 177-181.
35.. Uddandarao, D. P., & Vadlamani, R. K. (2025). Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs. arXiv preprint arXiv:2511.07484.
36. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
37.. Md Al Rafi. (2024). AI-Driven Fraud Detection Using Self-Supervised Deep Learning for Enhanced Customer Identity Modeling. International Journal of Humanities and Information Technology (IJHIT), 6(1), 8–18.
38.Yang, L. J., Chiu, H. T., & Hsu, P. F. (2015). Data Stream Mining for Fraud Detection in Financial Transactions. IEEE Transactions on Knowledge and Data Engineering. (cited by Muthusamy et al.) njhcair.org+1
