Bank Deposit Prediction Using Ensemble Learning

Abstract

Bank deposit is one of the vital issues for any financial institution. It is very challenging to predict a customer if he/she can be a depositor by analyzing related information. Some recent reports demonstrate that economic depression and the continuous decline of the economy negatively impact business organizations and banking sectors. Due to such economic depression, banks cannot attract a customer's attention. Thus, marketing is preferred to be a handy tool for the banking sector to draw customers' attention for a term deposit. The purpose of this paper is to study the performance of ensemble learning algorithms which is a novel approach to predict whether a new customer will have a term deposit or not. A Portuguese retail bank data is used for our study, containing 45,211 phone contacts with 16 input attributes and one decision attribute. The data are preprocessed by using the Discretization technique. 40,690 samples are used for training the classifiers, and 4,521 samples are used for testing. In this work, the performance of the three mostly used classification algorithms named Support Vector Machine (SVM), Neural Network (NN), and Naive Bayes (NB) are analyzed. Then the ability of ensemble methods to improve the efficiency of basic classification algorithms is investigated and experimentally demonstrated. Experimental results exhibit that the performance metrics of Neural Network (Bagging) is higher than other ensemble methods. Its accuracy, sensitivity, and specificity are 96.62%, 97.14%, and 99.08%, respectively. Although all input attributes are considered in the classification method, in the end, a descriptive analysis has shown that some input attributes have more importance for this classification. Overall, it is shown that ensemble methods outperformed the traditional algorithms in this domain. We believe our contribution can be used as a depositor prediction system to provide additional support for bank deposit prediction.

Predicting customer deposits with machine learning algorithms: evidence from Tunisia

“…(2020) in their proposed study applied logistic regression, random forest and light GBM to predict long-term deposit possibilities. Patway et al . (2021) used three classification techniques, which are the Naïve Bayes, ANN and support vector machine.…”

“…The intermediary role of banks gives the possibility to make profits from the difference between the interests earned on loans and the cost of funds acquired from the banks' sources of financing. The availability of sufficient financial resources is largely dependent on customer deposits (Patway et al ., 2021). This last source is obtained at low rates in comparison to any other source of funds, such as the money market and debt issuance.…”