Exploring the Impact of Predictive Analytics on Decision Making and Efficiency in the Banking Industry

Business use of analytical technologies has been on the rise in recent years as a key strategic set of tools for managing big data. The banking sector, in particular, experienced considerable interaction with customers using several channels that led to a substantial increase in data collected, leading to urgent need to integrate more and more analytical tools to better understand their customer’s needs. Predictive analytics technologies have become an increasingly key tool in the banking sector as more banks realize that predictive analytics enables them to make intelligent decisions and create better customer experiences. Yet, the adoption rate is relatively slow, and banks are only beginning to scratch the surface of such technologies, especially in developing countries. In this research we seek to provide the reader with the fundamental predictive analytics literature review while seeking to explore how predictive analytics technologies can be used to enhance the data-driven decision-making process and efficiency. Using data collected from employees who are already using predictive analytics, this research explores user perception and usage behavior of these sets of software and tools. In fact, the results of this work do not only uncover some key potential benefits of predictive analytics but also shed some light on how predictive analytics can be used and managed in the banking industry to enhance efficiency and decision making.