AI-Driven Cloud-Native Enterprise Platforms for Financial Decision Support and Real-Time Risk Management
DOI:
https://doi.org/10.64235/grwe8r09Keywords:
Artificial Intelligence, Cloud-Native Computing, Enterprise Platforms, Financial Decision Support, Real-Time Risk Management, Machine Learning, Predictive Analytics, Cloud Security, Financial Risk Analytics, MicroservicesAbstract
AI-driven cloud-native enterprise platforms are transforming financial decision support by combining artificial intelligence, scalable
cloud infrastructure, real-time analytics, and automated risk management into integrated digital ecosystems. Traditional financial
systems often struggle with fragmented data, delayed risk identification, rigid infrastructure, and limited adaptability to rapidly
changing market conditions. This paper proposes an AI-driven cloud-native enterprise platform that integrates machine learning,
predictive analytics, real-time data processing, containerized services, microservices architectures, application programming
interfaces, and automated risk intelligence to support timely and reliable financial decisions. The proposed approach enables
continuous ingestion and processing of financial transactions, market indicators, customer behavior, operational events, and
external risk signals. Machine learning models can identify anomalies, predict financial risks, classify potential threats, and generate
decision-support recommendations. Cloud-native scalability allows computational resources to dynamically adapt to workload
fluctuations, while security and governance mechanisms protect sensitive financial information. The methodology emphasizes
modular architecture, real-time data pipelines, AI model development, risk scoring, explainability, continuous monitoring, and
automated deployment. The resulting framework can improve decision speed, predictive capability, operational resilience,
scalability, and risk visibility. It also supports enterprise financial institutions in moving from reactive risk management toward
proactive, continuously adaptive, and intelligence-driven decision-making.
References
Wang, Y. (2024). Abnormal behavior identification of
enterprise cloud platform financial system based
on artificial neural network. Computers & Electrical
Engineering, 115, 109110. https://doi.org/10.1016/j.
compeleceng.2024.109110
Gopinathan, V. R. (2023). Intelligent Cloud Security
through Continuous Threat Detection and Risk
Assessment. International Research Journal of
Innovative Engineering, 7(6), 13571-13581.
Mali, R. K. (2026, July). AI-Driven Cloud-Native Banking
Platforms: A Scalable Architecture for Real-Time
Financial Services. In 2026 International Conference
on Intelligent and Sustainable AI Systems (ICOSAAS)
(pp. 749-756). IEEE.
Bellundagi, M. (2023). Design of an Intelligent Clinical
Decision Support System Using Machine Learning
Techniques. International Journal of Research and
Applied Innovations, 6(6), 10075-10081.
Sabin Begum, R., & Sugumar, R. (2019). Novel entropybased
approach for cost-effective privacy preservation
of intermediate datasets in cloud. Cluster Computing,
22(Suppl 4), 9581-9588.
Poranki, U. (2026). From Connectivity Monetizationto Network Developer Ecosystems: A Conceptual
Framework for Reclaiming the 6G Value Layer.
International Journal of Computer Information
Systems and Industrial Management Applications,
18(6s), 187-195.
Raja, G. V. (2020). Metadata gets a makeover: The machine
learning approach. International Journal of Computer
Technology and Electronics Communication, 3(6),
2900-2903.
Soundappan, S. J. (2023). Designing Intelligent Enterprise
Platforms Using Machine Learning Driven API
Engineering and Cloud Native Security. International
Journal of Research Publications in Engineering,
Technology and Management (IJRPETM), 6(4), 9074-
9081.
Gangavarapu, R., Kundurthy, O. H., Shirdi, A., Vikram,
S., Eripilla, J., & Jonnalagadda, A. K. (2025, July).
Model-Centric Data Validation: A Feedback-Loop
Approach to Dynamic Quality Control. In 2025 3rd
World Conference on Communication & Computing
(WCONF) (pp. 1-7). IEEE.
Vemireddy, S. (2022). Modernizing enterprise financial
platforms through distributed cloud architectures.
International Journal of Science, Research and
Technology (IJSRAT), 5(2), 7420–7426.
Mathew, A., & Romasco, L. (2024). Forensic Investigation
of Artificial Intelligence Systems. Research Updates
in Mathematics and Computer Science Vol. 4, 154-164.
Anand, L. (2022). Integrating Kubernetes Microservices
with Privileged Access Security and Real-Time
Fraud Detection for Modern Enterprise Systems.
International Journal of Research Publications
in Engineering, Technology and Management
(IJRPETM), 5(5), 7453-7461.
Omi, M. S. H., Ara, J., Ali, M. M., Hoque, M. R., Ferdausi, S.,
Fatema, K., ... & Bijoy, M. H. I. (2025, July). Integrating
Deep Neural Networks with Explainable AI for
Precise Brain Tumor Detection and Classification.
In 2025 International Conference on Quantum
Photonics, Artificial Intelligence, and Networking
(QPAIN) (pp. 1-6). IEEE.
Bandaru, P. K. (2024). Testing multi-ECU communication
networks in software-defined vehicles. International
Journal of Research and Applied Innovations, 7(4),
11178–11183.
Purella, S. (2025). Zero-trust architecture in distributed
financial ecosystems. International Journal of
Computing and Engineering, 7(20), 11–26.
Chundi, V. R. K. (2025). AI-based Sustainable Vehicle
Monitoring System for Existing Internal Combustion
Vehicles. London Journal of Research In Computer
Science and Technology, 25(3), 1-7.
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.
Pasumarthi, H. (2024). AI-driven forecasting and
optimization in distributed systems: Lessons from
retail, lending, and healthcare platforms. International
Journal of Research and Applied Innovations, 7(3),
10786-10790.
Mohan, A. (2025). Breaking down attribution modeling
in predictive analytics. World Journal of Advanced
Engineering Technology and Sciences, 15(02), 2851-
2859.
Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O.,
Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo,
T. E., Adegoke, O. S., Jamiu, A., Michael, O. B.,
Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P.
M., & Abd-Rouf, A. B. (2024). From molecular profiling
to predictive algorithms: A conceptual machinelearning
framework for mechanism-informed therapy
selection in multidrug-resistant cancer. International
Journal of Science, Research and Technology (IJSRAT),
7(3), 12085–12101.
Yepuri, V. K., Polamarasetty, V. K., Donthi, S., &
Gondi, A. K. R. (2023). Containerization of a
polyglot microservice application using Docker and
Kubernetes.arXiv preprint arXiv:2305.00600
Vimal Raja, G. (2024). Intelligent data transition in
automotive manufacturing systems using machine
learning. International Journal of Multidisciplinary
and Scientific Emerging Research, 12(2), 515-518.
Rehan, H., Sunkara, G., Sannamuri, V., Malik, M., Jayabalan,
K., & Shanthi, K. (2025, September). DeepShield:
Privacy-preserving malware classification using split
learning. In 2025 6th International Conference on
Electronics and Sustainable Communication Systems
(ICESC) (pp. 1812-1819). IEEE.
Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy
c-means clustering and elliptic curve cryptography
using privacy preserving in cloud. International
Journal of Business Intelligence and Data Mining,
15(3), 273-287.
Padmanabham, S. (2022). Enterprise identity and
access management architecture for large financial
institutions. International Journal of Research and
Applied Innovations, 5(1), 9486–9490.
Khan, A. A., Yang, J., Laghari, A. A., Baqasah, A. M.,
Alroobaea, R., Ku, C. S., Alizadehsani, R., Acharya,
U. R., & Por, L. Y. (2025). BAIoT-EMS: Consortium
network for small-medium enterprises management
system with blockchain and augmented intelligence
of things. Engineering Applications of Artificial
Intelligence, 141, 109838. https://doi.org/10.1016/j.
engappai.2024.109838
Mohile, A., Yadav, A. L., Mukherjee, U., Kapoor, R., Attri,
V., & Reddy, R. R. (2026, May). Ethical AI Framework
for Protecting Human Rights in Digital Surveillance
Systems. In 2026 International Conference onComputational Robotics, Testing and Engineering
Evaluation (ICCRTEE) (pp. 1-6). IEEE.
Patel, K., Hariharan, A., & Sucharitha, R. (2025, August).
Implementing Next-Gen Predictive Maintenance in
Industrial Machineries: A Comparative Analysis of
Small, Medium & Large Enterprises. In 2025 IEEE
Technology and Engineering Management Society
Conference-Global (TEMSCON Global) (pp. 1-3). IEEE.
Alam, A., Gazi, M. S., Abdullah, S. M., Tasnim, M.,
Himeluzzaman, M., Nabil, M. A., ... & Akter, S.
(2021). Quantum-resilient federated intrusion
detection: A hybrid quantum-classical framework for
safeguarding US critical infrastructure in the postquantum
era. International Journal of Advances in
Signal and Image Sciences, 7(1), 57–72.
Muppidi, N. K. R., Divi, V. R., Gulapalli, S., Rachamalla,
S., Tatavarthi, S., & Lakshmi, M. A. (2026, April).
CostAgent: Self-Improving Autonomous LLMBased
Orchestration for Cost-Optimal Cloud Data
Processing at Scale. In 2026 International Conference
on Artificial Intelligence, Systems, and Emerging
Technologies (ICAISET) (pp. 1-6). IEEE.
Cao, S. S., Jiang, W., Lei, L. G., & Zhou, Q. C. (2024).
Applied AI for finance and accounting: Alternative
data and opportunities. Pacific-Basin Finance Journal,
84, 102307. https://doi.org/10.1016/j.pacfin.2024.102307
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