Skip to main content

Secure Multi-Cloud Data Orchestration Frameworks for Digital Banking Ecosystems

Abstract

Secure Multi-Cloud Data Orchestration Frameworks for Digital Banking Ecosystems reflect the increasing demand for data services and complex workloads in the digital banking ecosystem. Service and data integration across clouds is a prominent requirement but sharing and cross-cloud geo-replication of sensitive data introduce security and privacy risks that control dispositions across cloud service providers do not adequately address. In this work, Security Data Orchestration in Multi-Cloud Banking Ecosystem proposes an architecture and a Data Orchestration in Multi-Cloud Banking Ecosystem Methodology for achieving a cloud-agnostic Control Plane. The comprehensive control set satisfies independent compliance frameworks and enables secure access to sensitive data while satisfying data policies, residency requirements, and regulatory considerations.

 

Given the interest and demand for Banking-as-a-Service from within the finance industry, a clear implementation roadmap and criteria enable banking institutions to plan, design, and secure a Service “Orchestration” and/or “Data Orchestration” capability. Security Data Orchestration in Multi-Cloud Banking Ecosystem present the Trade-Offs for Centralized, Federated, and Hybrid Architectures of Data Orchestration in Multi-Cloud Banking Ecosystem and Information and Data Aspects of Control Plane.

References

[1] Adegbite, M. A. Data privacy and data security challenges in digital finance. Journal of Digital Security and Forensics, 2(1), 6–19.
[2] Goutham Kumar Sheelam, "Semiconductor Innovation for Edge AI: Enabling Ultra-Low Latency in Next-Gen Wireless Networks," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.111258
[3] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
[4] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
[5] Arasu, A., & Kaushik, R. (2014). Data cleansing: A context dependent approach. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 135–146.
[6] Rongali, S. K. (2020). Predictive Modeling and Machine Learning Frameworks for Early Disease Detection in Healthcare Data Systems. Current Research in Public Health, 1(1), 1-15.
[7] Armbrust, M., Das, T., Davidson, A., Ghodsi, A., Or, A., Rosen, J., Stoica, I., Wendell, P., Xin, R., & Zaharia, M. (2021). Delta Lake: High-performance ACID table storage over cloud object stores. Proceedings of the VLDB Endowment, 13(12), 3411–3424.
[8] Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[9] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
[10] Chava, K., Chakilam, C., & Recharla, M. (2021). Machine Learning Models for Early Disease Detection: A Big Data Approach to Personalized Healthcare. International Journal of Engineering and Computer Science, 10(12), 25709–25730. https://doi.org/10.18535/ijecs.v10i12.4678
[11] Babcock, J., Chaudhuri, S., & Das, G. (2004). Dynamic sample selection for approximate query processing. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, 539–550.
[12] Sriram, H. K. (2022). Advancements in Credit Score Analytics using Deep Learning and Predictive Modeling Techniques. Available at SSRN 5255128.
[13] Bifet, A., & Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining, 443–448.
[14] Muthusamy, S., Kannan, S., Lee, M., Sanjairaj, V., Lu, W. F., Fuh, J. Y., ... & Cao, T. (2021). Cover Image, Volume 118, Number 8, August 2021. Biotechnology and Bioengineering, 118(8), i-i.
[15] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
[16] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments (January 20, 2021).
[17] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
[18] Dwaraka Nath Kummari. (2022). Fiscal Policy Simulation Using AI And Big Data: Improving Government Financial Planning. Kurdish Studies, 10(2), 934–945. https://doi.org/10.53555/ks.v10i2.3855
[19] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
[20] Gadi, A. L. The Role of Digital Twins in Automotive R&D for Rapid Prototyping and System Integration.
[21] Das, T., Zhu, A., Li, S., Narayanamurthy, S., & Bhat, P. (2013). Distributed and fault-tolerant streaming computation in Spark. Proceedings of the ACM Symposium on Cloud Computing, 1–12.
[22] Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037
[23] Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.
[24] Paleti, S. (2022). Financial Innovation through AI and Data Engineering: Rethinking Risk and Compliance in the Banking Industry. Available at SSRN 5250726.
[25] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., & Vogels, W. (2007). Dynamo: Amazon’s highly available key-value store. Proceedings of the 21st ACM Symposium on Operating Systems Principles, 205–220.
[26] Sriram, H. K., ADUSUPALLI, B., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks.
[27] Dwork, C. (2008). Differential privacy: A survey of results. Proceedings of the 5th International Conference on Theory and Applications of Models of Computation, 1–19.
[28] Varri, D. B. S. (2021). Cloud-Native Security Architecture for Hybrid Healthcare Infrastructure. Available at SSRN 5785982.
[29] Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16.
[30] Dwaraka Nath Kummari,. (2022). Machine Learning Approaches to Real-Time Quality Control in Automotive Assembly Lines. Mathematical Statistician and Engineering Applications, 71(4), 16801–16820. Retrieved from https://philstat.org/index.php/MSEA/article/view/2972
[31] Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284.
[32] Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
[33] Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
[34] Meda, R. Enabling Sustainable Manufacturing Through AI-Optimized Supply Chains.
[35] Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. Proceedings of the 19th ACM Symposium on Operating Systems Principles, 29–43.
[36] Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
[37] Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1–13. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1357
[38] Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
[39] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
[40] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2022). AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents (February 07, 2022).
[41] Hellerstein, J. M., Haas, P. J., & Wang, H. J. (1997). Online aggregation. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, 171–182.
[42] Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
[43] Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. Proceedings of the 2008 IEEE International Conference on Data Mining, 263–272.
[44] Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
[45] Davuluri, P. S. L. N. (2021). Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence. Journal of International Crisis and Risk Communication Research , 339–354. https://doi.org/10.63278/jicrcr.vi.3636
[46] Meda, R. (2022). Integrating Edge AI in Smart Factories: A Case Study from the Paint Manufacturing Industry. International Journal of Science and Research (IJSR), 1473-1489.
[47] Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.
[48] Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
[49] Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152.
[50] Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to .NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1–17.
[51] Kleppmann, M. (2017). Designing data-intensive applications. O’Reilly Media.
[52] Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
[53] Lahiri, M., & Venkatasubramanian, S. (2013). Robust record linkage. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 101–112.
[54] Rongali, S. K. (2021). Cloud-Native API-Led Integration Using MuleSoft and .NET for Scalable Healthcare Interoperability. Available at SSRN 5814563.
[55] Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets (2nd ed.). Cambridge University Press.
[56] Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.
[57] Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.
[58] Meda, R. (2021). Digital Infrastructure for Predictive Inventory Management in Retail Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
[59] Lin, J., Kolcz, A., & Szymanski, B. K. (2012). Large-scale machine learning at Twitter. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 793–804.
[60] Sheelam, G. K. Power-Efficient Semiconductors for AI at the Edge: Enabling Scalable Intelligence in Wireless Systems. International Journal of Innovative Research in Electrical, Elec-tronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[61] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
[62] Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Legal and Ethical Considerations for Hosting GenAI on the Cloud. International Journal of AI, BigData, Computational and Management Studies, 2(2), 28-34.
[63] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. Proceedings of the International Conference on Learning Representations, 1–12.
[64] Ramesh Inala. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280–304. Retrieved from https://migrationletters.com/index.php/ml/article/view/11982
[65] Montoya, D. Y., Neto, A. M., & da Silva, A. S. (2016). A survey of entity resolution in big data. Journal of Big Data, 3(1), 1–22.
[66] Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks.
[67] Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, 1–7.
[68] Varri, D. B. S. (2022). AI-Driven Risk Assessment and Compliance Automation in Multi-Cloud Environments. Available at SSRN 5774924.
[69] Zaharia, M., Das, T., Li, H., Shenker, S., & Stoica, I. (2012). Discretized streams: Fault-tolerant streaming computation at scale. Proceedings of the 24th ACM Symposium on Operating Systems Principles, 423–438.
[70] Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1–17.
[71] Zhai, C., & Massung, S. (2016). Text data management and analysis: A practical introduction to information retrieval and text mining. ACM & Morgan Claypool.
[72] Davuluri, P. N. (2020). Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking.
[73] Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146.
[74] Keerthi Amistapuram , "Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2020.81209
[75] Goutham Kumar Sheelam. (2022). Reconfigurable Semiconductor Architectures For AI-Enhanced Wireless Communication Networks. Kurdish Studies, 10(2), 1027–1040. https://doi.org/10.53555/ks.v10i2.3867
[76] Batarseh, F. A., & Yang, R. (2019). Federal data science: Transforming government and society. Academic Press.
[77] Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
[78] Bhasin, H., & Bhatia, P. (2020). Clickstream data mining for web analytics and customer behavior modeling: A review. ACM Computing Surveys, 53(6), 1–34.
[79] Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1360
[80] Uday Surendra Yandamuri. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 472–483. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8005
[81] Abedjan, Z., Golab, L., & Naumann, F. (2016). Profiling relational data: A survey. The VLDB Journal, 24(4), 557–581.
[82] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111
[83] Dwaraka Nath Kummari. (2022). AI-Driven Audit Frameworks For Enhancing Compliance In Modern Manufacturing Systems. Migration Letters, 19(S8), 2150–2177. Retrieved from https://migrationletters.com/index.php/ml/article/view/11912
[84] Davuluri, P. N. Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence.
[85] Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2021). Fraud analytics using descriptive, predictive, and social network techniques: A guide to data science for fraud detection (2nd ed.). Wiley.
[86] Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609
[87] Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org (Book manuscript).
[89] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
[90] Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
[91] Ahmad, M. A., Eckert, C., & Teredesai, A. (2018). Interpretable machine learning in healthcare. Proceedings of the ACM Conference on Health, Informatics, and Data Science, 1–10.
[92] Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Available at SSRN 5532601.
[93] Aljabre, A. (2019). Cloud computing security in healthcare. Journal of King Saud University – Computer and Information Sciences, 31(1), 10–18.
[94] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
[94] Akanfe, O. A. (2022). Advancing digital financial inclusion: Data privacy, regulatory compliance, and cross-country cultural values in digital payment systems use (Doctoral dissertation, The University of Texas at San Antonio).
[95] Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 444–455. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7905
[96] Kothapalli Sondinti, L. R., & Syed, S. (2022). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics, 1(1), 1223. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1223
[97] Crisanto, J. C., Leuterio, C. B., Prenio, J., & Yong, J. Regulating AI in the financial sector: Recent developments and main challenges. FSI Insights on Policy Implementation, (63).