Researcher: Tselahale Serongwa, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg
The banking sector needs the ability to categorize the customer data they possess to enable business intelligence analytics and improve their marketing strategies. They require trivial automated models that yield interpretable results to comply with the financial regulations. An XGBoost model is built to determine the minimum number of attributes with the greatest impact in determining the potential of the 45211 customers to subscribe to a term deposit. The Synthetic Minority Over-sampling Technique was used to balance the dataset and eleven important attributes with 79% prediction power from 39 attributes. The model had an f1-score and testing accuracy of 93% whilst the model’s reliability was 86%. Four clusters were determined using the k-prototype clustering technique to group customers for tailored marketing strategies. It was determined that the bank had more chances of getting business from the 6859 customers clustered in the three most valuable clusters and should consider cheaper marketing options for the remainder of their customers.
