Unified Apache-Based AI Cloud Analytics for Financial Intelligence across Smart Waste Management and Healthcare

Andreas John Christopher

doi:10.15662/IJRAI.2024.0705013

Authors

Andreas John Christopher Independent Researcher, Spain Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0705013

Keywords:

Artificial Intelligence, Cloud Analytics, Apache Spark, Apache Iceberg, Financial Intelligence, Smart Waste Management, Healthcare Data Analytics

Abstract

The rapid growth of data across financial services, smart waste management, and healthcare systems has created a strong demand for unified, scalable, and intelligent cloud analytics platforms. This paper presents a Unified Apache-Based AI Cloud Analytics framework designed to deliver financial intelligence while supporting cross-domain applications in smart waste management and healthcare. The proposed architecture leverages Apache ecosystem technologies, including Apache Spark for distributed processing, Apache Kafka for real-time data ingestion, and Apache Iceberg for reliable data lakehouse management on cloud infrastructure. Artificial intelligence and machine learning models are integrated to enable predictive analytics, anomaly detection, and decision support across heterogeneous datasets. Security and governance mechanisms such as role-based access control, encryption, and audit logging are incorporated to ensure data privacy and regulatory compliance. Experimental evaluation demonstrates low-latency analytics, high scalability, and robust predictive performance across financial risk analysis, waste optimization forecasting, and healthcare data insights. The results highlight the effectiveness of a unified AI-driven cloud analytics approach in enabling intelligent, secure, and scalable data-driven decision-making across multiple critical domains.

References

1. Armbrust, M., et al. (2020). Iceberg: High-performance format for huge analytic tables.

2. Davenport, T. H., & Harris, J. G. (2017). Competing on Analytics. Harvard Business Review Press.

3. Paul, D. et al., "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach," Journal of Science & Technology, vol. 2, no. 3, Sept. 8, (2021). https://thesciencebrigade.com/jst/article/view/382

4. Marston, S., et al. (2011). Cloud computing — The business perspective. Decision Support Systems.

5. Sharma, A., Kabade, S., & Kagalkar, A. (2024). AI-Driven and Cloud-Enabled System for Automated Reconciliation and Regulatory Compliance in Pension Fund Management. International Journal of Emerging Research in Engineering and Technology, 5(2), 65-73.

6. Rajurkar, P. (2023). Waste-to-Resource Networks for Inorganic Chemical Manufacturing A Case Study. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5944-5953.

7. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.

8. Stonebraker, M., et al. (2018). The architecture of modern data systems. Communications of the ACM.

9. Vasugi, T. (2022). AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments. International Journal of Research and Applied Innovations, 5(4), 7368-7376.

10. Zaharia, M., et al. (2010). Spark: Cluster computing with working sets.

11. 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.

12. Meka, S. (2023). Building Digital Banking Foundations: Delivering End-to-End FinTech Solutions with Enterprise-Grade Reliability. International Journal of Research and Applied Innovations, 6(2), 8582-8592.

13. Kumar, R., Christadoss, J., & Soni, V. K. (2024). Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 351-366.

14. Hossain, A., ataur Rahman, K., Zerine, I., Islam, M. M., Hasan, S., & Doha, Z. (2023). Predictive Business Analytics For Reducing Healthcare Costs And Enhancing Patient Outcomes Across US Public Health Systems. Journal of Medical and Health Studies, 4(1), 97-111.

15. Ngai, E. W. T., et al. (2011). The application of data mining techniques in financial fraud detection. Expert Systems with Applications.

16. Praveen Kumar Reddy Gujjala. (2023). Advancing Artificial Intelligence and Data Science: A Comprehensive Framework for Computational Efficiency and Scalability. IJRCAIT, 6(1), 155-166.

17. Dhanorkar, T., Vijayaboopathy, V., & Das, D. (2020). Semantic Precedent Retriever for Rapid Litigation Strategy Drafting. Journal of Artificial Intelligence & Machine Learning Studies, 4, 71-109.

18. 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.

19. Abadi, D. J., et al. (2009). The design of the Borealis stream processing engine.

20. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

21. Kusumba, S. (2024). Delivering the Power of Data-Driven Decisions: An AI-Enabled Data Strategy Framework for Healthcare Financial Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7799-7806.

22. Udayakumar, R., Chowdary, P. B. K., Devi, T., & Sugumar, R. (2023). Integrated SVM-FFNN for fraud detection in banking financial transactions. Journal of Internet Services and Information Security, 13(3), 12-25.

23. Christadoss, J., Yakkanti, B., & Kunju, S. S. (2023). Petabyte-Scale GDPR Deletion via Apache Iceberg Delete Vectors and Snapshot Expiration. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.

24. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.

25. Sudhakara Reddy Peram, Praveen Kumar Kanumarlapudi, Sridhar Reddy Kakulavaram. (2023). Cypress Performance Insights: Predicting UI Test Execution Time Using Complexity Metrics. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 6(1), 167-190.

26. Chandra Sekhar Oleti, " Real-Time Feature Engineering and Model Serving Architecture using Databricks Delta Live Tables" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.746-758, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT23906203

27. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.

28. 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.

29. Karnam, A. (2024). Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006

30. Grolinger, K., et al. (2018). Data management in cloud environments: NoSQL and NewSQL systems.

31. Nagarajan, G. (2022). Advanced AI–Cloud Neural Network Systems with Intelligent Caching for Predictive Analytics and Risk Mitigation in Project Management. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7774-7781.

32. Patel, P., & Shah, N. (2019). AI-driven financial analytics architectures.