Cloud Data Engineering for AI-Driven Healthcare and Financial Risk
Keywords:
Next-Generation Cloud Data Engineering for AI-Powered Healthcare Platforms and Financial Risk Intelligence; Data Ownership; Security; Data Governance; Meta Data Management; Data Pipeline; Interoperability; Scalability; Data Processing; Streaming Architecture; Data Serving; Decision Support; Patient-Centric Analytics; Personalization; Data Model.Abstract
Next-Generation Cloud Data Engineering provides scalable, interoperable, and secure data foundations for AI-Powered Healthcare Platforms and Financial Risk Intelligence. AI-powered platforms in healthcare and finance promise significant societal benefits, but shortcomings in cloud data engineering can thwart success. Resolving these concerns involves five dimensions. First, data ingestion, integration, and orchestration pipelines for batch and streaming use cases should accommodate distinct architectural patterns and be complemented by well-defined orchestration strategies. Second, feature stores must reduce serving latency, allowing machine-learning features to be reused across multiple models while supporting versioning, lineage, and validation. Third, model-lifecycle-management solutions should facilitate continuous, automated model training and evaluation. Fourth, data foundations for healthcare platforms must support clinical decision support, patient-centric analytics, and personalization across federated requirements. Fifth, financial-risk-intelligence platforms require AI models for market, credit, and operational-resilience risk, with dedicated solutions addressing model quality, data risk, and compliance. Addressing these aspects reduces the implementation burden and helps ensure that the anticipated benefits of AI-powered healthcare and financial platforms are realized.
References
1. Ahsan, M. M., Luna, S. A., Siddique, Z., & Islam, M. M. (2021). Machine learning-based disease diagnosis: A comprehensive review. Healthcare, 9(6), 717.
2. Ali, O., Abdelbaky, M., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in healthcare: Applications, challenges, and future research. Healthcare, 11(17), 2458.
3. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
4. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2021). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
5. Benjamens, S., Dhunnoo, P., & Meskó, B. (2021). The state of artificial intelligence-based FDA-approved medical devices and algorithms. NPJ Digital Medicine, 4(1), 1–8.
6. Bohr, A., & Memarzadeh, K. (Eds.). (2021). Artificial intelligence in healthcare. Academic Press.
7. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
8. Brintrup, A., Pak, J., Pearson, S., & Tiwari, A. (2022). Data engineering challenges for industrial AI systems. Computers in Industry, 137, 103594.
9. Chen, M., Decary, M., & Liu, X. (2022). Artificial intelligence in healthcare: A review of opportunities and challenges. Artificial Intelligence in Medicine, 128, 102290.
10. Choi, T. M., Kumar, S., Yue, X., & Chan, H. L. (2022). Disruptive technologies and operations management in healthcare and finance. Annals of Operations Research, 308(1), 1–22.
11. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2021). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 49(1), 24–42.
12. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
13. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., ... & Wamba, S. F. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, 102642.
14. Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: Data engineering for cloud analytics systems. Future Generation Computer Systems, 129, 65–79.
15. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2021). A guide to deep learning in healthcare. Nature Medicine, 27(11), 1899–1912.
16. Gao, J., Wang, H., & Shen, H. (2022). Smartly handling cloud-native applications with Kubernetes. ACM Computing Surveys, 54(8), 1–36.
17. Oladosu, S. A., Ige, A. B., Ike, C. C., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2022). Revolutionizing data center security: Conceptualizing a unified security framework for hybrid and multi-cloud data centers. Open Access Research Journal of Science and Technology, 5(2), 086-076.
18. Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., & Buyya, R. (2022). AI for next-generation cloud computing: Vision, challenges and future directions. Future Generation Computer Systems, 135, 253–274.
19. Glicksberg, B. S., Johnson, K. W., & Dudley, J. T. (2021). The next generation of precision medicine using artificial intelligence. Nature Reviews Genetics, 22(9), 587–602.
20. Kotu, V., & Deshpande, B. (2022). Data science: Concepts and practice (3rd ed.). Morgan Kaufmann.
21. Kshetri, N. (2021). Artificial intelligence in finance: Applications, challenges, and opportunities. IT Professional, 23(4), 15–19.
22. L’heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2021). Machine learning with big data: Challenges and approaches. IEEE Access, 9, 48376–48397.
23. Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021). Federated learning for healthcare: A survey. ACM Computing Surveys, 55(3), 1–37.
24. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
25. Wamba, S. F., Queiroz, M. M., Trinchera, L., & Fosso Wamba, S. (2021). Big data analytics in operations and supply chain management: Emerging trends and future directions. International Journal of Production Economics, 232, 107972.
26. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2021). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 119.
27. Sadiku, M. N. O., Ashaolu, T. J., Musa, S. M., & Perry, R. (2021). Cloud computing in healthcare. International Journal of Scientific Advances, 2(3), 255–258.
28. Reddy, V. A. R. (2023). Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7967-7978.
29. Sharma, A., Sharma, P., & Kumar, R. (2022). Cloud-native architectures for artificial intelligence applications: A survey. Journal of Systems Architecture, 127, 102523.
30. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2022). Edge computing: Vision, applications, and challenges. IEEE Internet of Things Journal, 9(2), 895–915.
31. Topol, E. (2021). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
32. Vaid, A., Somani, S., Russak, A. J., Freitas, J. K., Chaudhry, F. F., Paranjpe, I., Johnson, K. W., Lee, S., Miotto, R., Richter, F., & Glicksberg, B. S. (2021). Machine learning to predict mortality and critical events in COVID-19 patients. NPJ Digital Medicine, 3(1), 75.
33. Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.
34. Varghese, B., & Buyya, R. (2022). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861.
35. Verma, A., Bhattacharya, P., & Kumar, S. (2023). Cloud-based big data analytics for healthcare systems: A systematic review. Journal of King Saud University – Computer and Information Sciences, 35(7), 101611.
36. Wang, F., Casalino, L. P., & Khullar, D. (2021). Deep learning in medicine—Promise, progress, and challenges. JAMA Internal Medicine, 181(7), 889–890.
37. Kolla, T. (2024). Intelligent Discovery and Governance of Healthcare Data Assets Through AI-Powered Catalog Architectures. International Journal of Emerging Trends in Engineering and Management Research, 9(4), 16083.
38. Wang, S., Li, X., Zhang, Y., & Li, J. (2023). Artificial intelligence-enabled financial risk management: A systematic literature review. Expert Systems with Applications, 221, 119730.
39. Wazid, M., Das, A. K., Rodrigues, J. J. P. C., Shetty, S., & Jo, M. (2022). Security and privacy in cloud-assisted healthcare systems: A survey. IEEE Access, 10, 10211–10245.
40. Xu, M., David, J. M., & Kim, S. H. (2021). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Studies, 9(4), 90.
41. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
42. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2021). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
43. Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
44. Zhang, Y., Chen, X., & Li, J. (2022). Explainable artificial intelligence for healthcare: Methods, applications, and future directions. Artificial Intelligence Review, 55(8), 6209–6246.
45. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2022). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.
46. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
47. Albahri, A. S., Al-Obaidi, J. R., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., Salih, M. M., & Abdulkareem, K. H. (2022). Systematic review of artificial intelligence techniques in healthcare. Computer Methods and Programs in Biomedicine, 225, 107073.
48. Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1–38.
49. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2021). Artificial intelligence in healthcare: Past, present, and future. Stroke and Vascular Neurology, 6(2), 230–243.
50. Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2022). AI and data analytics in digital government: Research directions and future opportunities. Government Information Quarterly, 39(2), 101640.
51. Ahmed, E., Yaqoob, I., Hashem, I. A. T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2021). The role of big data analytics in cloud computing: A review. Journal of Network and Computer Applications, 180, 103032.
52. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30(4), 1011-1027.
53. Alzahrani, J. S., Alghamdi, A. M., & Alotaibi, F. M. (2022). Cloud-based artificial intelligence for healthcare data management: Opportunities and challenges. Healthcare, 10(11), 2235.
54. Bag, S., Yadav, G., Dhamija, P., & Kataria, K. K. (2022). Artificial intelligence and blockchain for healthcare supply chain resilience. Technological Forecasting and Social Change, 179, 121643.
55. Bhattacharya, P., Tanwar, S., Tyagi, S., & Kumar, N. (2022). Machine learning and cloud computing for healthcare 4.0: A comprehensive survey. Journal of Biomedical Informatics, 129, 104061.
56. Bitar, H., Chamas, M., & Kayssi, A. (2023). Cloud-native data engineering architectures for scalable AI applications. Future Generation Computer Systems, 145, 308–322.
57. Dinh, T. N., Thai, M. T., & Wang, Y. (2023). Artificial intelligence for financial risk management: Recent advances and future trends. Expert Systems with Applications, 213, 118986.
58. Ghosh, A., Guha, S., & Dutta, T. (2024). AI-enabled cloud analytics for intelligent healthcare systems: A review. Artificial Intelligence in Medicine, 149, 102782.
59. Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management.
60. Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., & Buyya, R. (2022). Transformative effects of IoT, blockchain and AI on cloud computing: Vision, trends and open challenges. Internet of Things, 20, 100118.
61. Hasan, M. K., Ghazali, K. H., & Islam, S. (2023). Explainable artificial intelligence for healthcare and financial analytics: A survey. IEEE Access, 11, 86524–86549.
62. Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, W. K. M., Alam, M., Shiraz, M., & Gani, A. (2021). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2021, 712826.
63. Li, X., Ding, Y., & Wang, H. (2023). Cloud data engineering for intelligent analytics: Architectures and applications. Journal of Cloud Computing, 12(1), 94.
64. Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
65. Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. (2022). Big data application in biomedical research and healthcare: A literature review. Biomedical Informatics Insights, 14, 1–14.
66. Naser, M. Z. (2022). Decentralized artificial intelligence in cloud environments: A review of emerging technologies. IEEE Access, 10, 107735–107756.
67. Pan, S., Zhang, S., Wang, H., & Li, X. (2024). Cloud-native machine learning pipelines for healthcare data analytics. Future Generation Computer Systems, 151, 221–236.
68. Sharma, R., Gupta, A., & Singh, P. (2023). Artificial intelligence-driven predictive analytics for financial risk assessment: A systematic review. Journal of Risk and Financial Management, 16(8), 356.
69. Sun, Z., Zhang, Y., & Wang, L. (2024). Data engineering frameworks for trustworthy AI in cloud healthcare ecosystems. IEEE Access, 12, 34791–34815.
70. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
71. Tuli, S., Gill, S. S., Xu, M., Bahsoon, R., & Buyya, R. (2023). HealthFog: An ensemble deep learning-based smart healthcare framework using cloud and edge computing. Future Generation Computer Systems, 138, 99–118.
72. Vohra, R., Sharma, P., & Kaur, H. (2024). AI-enabled cloud platforms for secure financial data analytics. Journal of Information Security and Applications, 77, 103561.
73. Wang, L., Li, Z., Chen, Y., & Xu, J. (2023). Secure cloud data engineering for intelligent healthcare and finance using artificial intelligence. IEEE Access, 11, 117402–117421.
74. Zhang, H., Liu, Y., Wang, X., & Chen, L. (2024). Intelligent cloud-based data engineering for AI-driven healthcare and financial services. Future Generation Computer Systems, 152, 410–425.
