Customer Churn Prediction in Banking Industry Using Power Bi

The development of technology in our modern day has led to the generation of huge data. This is evident by the 2.5 quintillions of data generated by persons connected to the Internet per day in 2020. With the expectation of 5.3 billion Internet users by 2023, complex and efficient tools, models, or approaches that will explore, analyze, and produce meaningful hidden information from huge data are needed. In recent years, machine learning techniques such as logistic regression, decision trees, and clustering are beginning to gain relevance, especially in churn prediction. Customer churn prediction is the process of determining the proportion of clients who avoid or might stop using or subscribing to a product or service offered by an organization or company. Though various prediction models have been proposed, most research attention has been given to measuring the efficiency of prediction models, rather than identifying its application for sustainable economic development. In this paper, we investigate the determining factor for customer attrition in the banking sector using Power BI. Dataset from United Bank of Africa (UBA), Nigeria was preprocessed with four key customer variables were used. The decision tree algorithm available in the Power Bi software was employed for training and testing. The results show that customer account balance is a key determining variable for churning. Furthermore, the results show that churning occurs less in male than female clients. This work will provide banks with useful knowledge on building effective customer retention strategies. Building an effective and accurate customer churn prediction model is an important research problem for both academics and practitioners.