Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Based Risk Management Frameworks for Financial Institutions

PDF
  • Dr. Elena Ferrari
Dr. Elena Ferrari
Professor of Information Engineering, University of Florence, Italy
Cover

Published 09-08-2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. E. Ferrari, “AI-Based Risk Management Frameworks for Financial Institutions”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 444–460, Aug. 2023, Accessed: Dec. 18, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/214

Abstract

Artificial intelligence (AI) uses the power of data to make decisions about the future. It processes large historical data repositories and deduces the patterns established in the data. It then uses the deduced patterns to predict future data points, often to a very high level of accuracy. One subset of AI is called machine learning (ML). ML can be classified as supervised, unsupervised, partially supervised, and reinforcement learning, based on the pattern that ML maps or learns. ML and AI have been revolutionary in the ever-increasing big data world. AI and ML have been used interchangeably with sufficient background that they fall under the technological paradigm currently shaping modern businesses, including the current and future business of banking.

PDF

Downloads

Download data is not yet available.

References

  1. Tamanampudi, Venkata Mohit. "NLP-Powered ChatOps: Automating DevOps Collaboration Using Natural Language Processing for Real-Time Incident Resolution." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 530-567.
  2. Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
  3. S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
  4. Pal, Dheeraj Kumar Dukhiram, et al. "Implementing TOGAF for Large-Scale Healthcare Systems Integration." Internet of Things and Edge Computing Journal 2.1 (2022): 55-102.
  5. Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
  6. J. Singh, “The Future of Autonomous Driving: Vision-Based Systems vs. LiDAR and the Benefits of Combining Both for Fully Autonomous Vehicles ”, J. of Artificial Int. Research and App., vol. 1, no. 2, pp. 333–376, Jul. 2021
  7. Gadhiraju, Asha. "Improving Hemodialysis Quality at DaVita: Leveraging Predictive Analytics and Real-Time Monitoring to Reduce Complications and Personalize Patient Care." Journal of AI in Healthcare and Medicine 1.1 (2021): 77-116.
  8. Gadhiraju, Asha. "Empowering Dialysis Care: AI-Driven Decision Support Systems for Personalized Treatment Plans and Improved Patient Outcomes." Journal of Machine Learning for Healthcare Decision Support 2.1 (2022): 309-350.
  9. Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
  10. J. Singh, “Understanding Retrieval-Augmented Generation (RAG) Models in AI: A Deep Dive into the Fusion of Neural Networks and External Databases for Enhanced AI Performance”, J. of Art. Int. Research, vol. 2, no. 2, pp. 258–275, Jul. 2022
  11. S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
  12. Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
  13. Tamanampudi, Venkata Mohit. "A Data-Driven Approach to Incident Management: Enhancing DevOps Operations with Machine Learning-Based Root Cause Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 419-466.