Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Predictive Analytics in Banking Using AI

Dr. Jing Li
Professor of Electrical Engineering, Tsinghua University, China

Published 01-10-2024

Keywords

  • Predictive Analytics,
  • Banking,
  • Banking Using AI

How to Cite

[1]
D. J. Li, “Predictive Analytics in Banking Using AI”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 108–121, Oct. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/195

Abstract

Banking is a data-driven sector. Especially with the paradigm shift from traditional to digital banking, the volume of data generated and analyzed has grown significantly. Predictive analytics, an area of analytics with the propensity to forecast future events, has a huge impact on the banking industry, and different aspects related to the banking sector can benefit significantly. Predictive analytics uses historical data to predict and anticipate future trends related to customers, industries, products, etc., making it easier for strategists and high-value decision-makers to predict and run their functions. Various studies have been conducted on different industries to see the importance of different tools and techniques of predictive analytics concerning the improvement in the sector. On a large scale, these studies concluded that predictive analytics can bring improvement and change to any sector based on the results obtained from solutions designed using predictive analytics tools.

Downloads

Download data is not yet available.

References

  1. 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.
  2. Pal, Dheeraj Kumar Dukhiram, et al. "AIOps: Integrating AI and Machine Learning into IT Operations." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 288-311.
  3. Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.
  4. J. Singh, “Robust AI Algorithms for Autonomous Vehicle Perception: Fusing Sensor Data from Vision, LiDAR, and Radar for Enhanced Safety”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 118–157, Apr. 2024
  5. Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
  6. Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
  7. Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
  8. Tamanampudi, Venkata Mohit. "AI-Powered NLP Agents in DevOps: Automating Log Analysis, Event Correlation, and Incident Response in Large-Scale Enterprise Systems." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 646-689.
  9. J. Singh, “The Ethical Implications of AI and RAG Models in Content Generation: Bias, Misinformation, and Privacy Concerns”, J. Sci. Tech., vol. 4, no. 1, pp. 156–170, Feb. 2023
  10. S. Kumari, “Optimizing Mobile Platform Security with AI-Powered Real-Time Threat Intelligence: A Study on Leveraging Machine Learning for Enhancing Mobile Cybersecurity”, J. of Art. Int. Research, vol. 4, no. 1, pp. 332–355, Jan. 2024.
  11. Praveen, S. Phani, et al. "Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management." Journal of Intelligent Systems & Internet of Things 13.1 (2024).
  12. Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
  13. Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.