摘要：(1) This study aims to predict the youth customers’ defection in retail banking. The samplecomprised 602 young adult bank customers. (2) The study applied Machine learning techniques,including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factorsto churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88%validated the findings. (4) Customer retention is one of the critical success factors for organizationsso as to enhance the business value. It is imperative for banks to predict the drivers of churn amongtheir young adult customers so as to create and deliver proactive enable quality services.
