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: Nov. 21, 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.

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