CLASSIFYING EFFECT OF E-BANKING SERVICE ON DEPOSIT MOBILIZATION USING MACHINE-LEARNING TECHNIQUES

Abstract

Identifying services that are more likely potential to E-banking product offering is an important issue. Cooperative Bank of Oromia S.C., being one of the former private banks in Ethiopia is offering E-Banking products. The main objective of this study is to apply machine learning algorithms for developing Deposit mobilization Performance prediction Model that forecast potential of E-banking channel service in Cooperative Bank of Oromia. This research follows experimental research. For modelling purpose, data was gathered from the institution head office. Since irrelevant features result in bad model performance, data pre processing was performed in order to determine the inputs to the model. This thesis investigates the creation and assessment of six machine learning algorithms to forecast deposit behavior from customers: CART, SVM, KNN, Naïve Bayes, Logistic Regression and Random Forest. Cross tables were used to show the results of precision calculations and confusion matrices used to evaluate the performance of these models. With an emphasis on the relevance of various attributes in predicting customer deposits, the suitability of various classification algorithms, the relative effectiveness of ensemble versus base learning models, and forecasting based on influential attributes, the study tackled three main research questions. Experimental results exhibit that, the ensemble learning model achieved 98.496% accuracy in categorizing deposits, outperforming individual algorithms like KNN (98.491%) and SVM (98.401%), emphasizing the superiority of ensemble methods for deposit mobilization prediction. Random Forest Classifier identified "other_debit," "gender," and "mobile banking" as the most significant predictors of deposit mobilization, with relevance scores of 20%, 18%, and 13% respectively. Moderately important features included "mobile_credit", "mobile_debit", "card_debit", and "marital_status", while "atm_card" and "other_credit" were negligible. Finally, this thesis shows the effectiveness of machine learning in financial prediction by offering a thorough comparison of six popular categorization methods. The result offer valuable insights for enhancing customer deposit strategies at CBO and potentially other banking institutions