Volume 11 • Issue 1 • PP: 79-88 • 2024
Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions
View PDF Abstract
Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry.
Keywords
References
[1] Moro, S., Cortez, P., & Rita, P. (2015). Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns. Neural Computing and Applications, 26(1), 131–139. https://doi.org/10.1007/s00521-014-1703-0
[2] Khafaga, D., Alhussan, A., El-kenawy, E.-S., Takieldeen, A., Hassan, T., Hegazy, E., Abdel, E., Ibrahim, A., & Abdelhamid, A. (2022). Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer. Computers, Materials & Continua, 73(1), 749–765. https://doi.org/10.32604/cmc.2022.029605
[3] Hosseini, S. (2021). A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing. Journal of Management Analytics, 8(2), 295–315. https://doi.org/10.1080/23270012.2021.1897956
[4] Hassib, E. M., El-Desouky, A. I., El-Kenawy, E.-S. M., & El-Ghamrawy, S. M. (2019). An Imbalanced Big Data Mining Framework for Improving Optimization Algorithms Performance. IEEE Access, 7, 170774–170795. https://doi.org/10.1109/ACCESS.2019.2955983
[5] Yan, C., Li, M., & Liu, W. (2020). Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network. Applied Soft Computing, 92, 106259. https://doi.org/10.1016/j.asoc.2020.106259
[6] Ładyżyński, P., Żbikowski, K., & Gawrysiak, P. (2019). Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Systems with Applications, 134, 28–35. https://doi.org/10.1016/j.eswa.2019.05.020
[7] Feng, Y., Yin, Y., Wang, D., & Dhamotharan, L. (2022). A dynamic ensemble selection method for bank telemarketing sales prediction. Journal of Business Research, 139, 368–382. https://doi.org/10.1016/j.jbusres.2021.09.067
[8] Xie, C., Zhang, J.-L., Zhu, Y., Xiong, B., & Wang, G.-J. (2023). How to improve the success of bank telemarketing? Prediction and interpretability analysis based on machine learning. Computers & Industrial Engineering, 175, 108874. https://doi.org/10.1016/j.cie.2022.108874
[9] Khan, M. Z., Munquad, S., & Rao, T. S. M. (2022). A Study on Improving Banking Process for Predicting Prospective Customers of Term Deposits using Explainable Machine Learning Models. In S. Bhalla, M. Bedekar, R. Phalnikar, & S. Sirsikar (Eds.), Proceeding of International Conference on Computational Science and Applications (pp. 93–103). Springer Nature. https://doi.org/10.1007/978-981-19-0863-7_7
[10] Ajay Ram, B., santosh Kumar, D. J., & Lakshmanarao, A. (2022). Improving Efficiency of Machine Learning Model for Bank Customer Data Using Genetic Algorithm Approach. In A. Khanna, D. Gupta, S. Bhattacharyya, A. E. Hassanien, S. Anand, & A. Jaiswal (Eds.), International Conference on Innovative Computing and Communications (pp. 649–657). Springer. https://doi.org/10.1007/978-981-16-2594-7_53
[11] Ghosh, S., Hazra, A., Choudhury, B., Biswas, P., & Nag, A. (2018). A Comparative Study to the Bank Market Prediction. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition (pp. 259–268). Springer International Publishing. https://doi.org/10.1007/978-3-319-96136-1_21
[12] Miguéis, V. L., Camanho, A. S., & Borges, J. (2017). Predicting direct marketing response in banking: Comparison of class imbalance methods. Service Business, 11(4), 831–849. https://doi.org/10.1007/s11628-016-0332-3
[13] Bank Term Deposit Predictions. (n.d.). [dataset]. Retrieved December 14, 2023, from https://www.kaggle.com/datasets/thedevastator/bank-term-deposit-predictions.
[14] Arora, A. S., Rajput, H., & Changotra, R. (2021). Current perspective of COVID-19 spread across South Korea: Exploratory data analysis and containment of the pandemic. Environment, Development and Sustainability, 23(5), 6553–6563. https://doi.org/10.1007/s10668-020-00883-y
[15] Eid, M. M., El-Kenawy, E.-S. M., Khodadadi, N., Mirjalili, S., Khodadadi, E., Abotaleb, M., Alharbi, A. H., Abdelhamid, A. A., Ibrahim, A., Amer, G. M., Kadi, A., & Khafaga, D. S. (2022). Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases. Mathematics, 10(20), Article 20. https://doi.org/10.3390/math10203845
[16] Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N., & Mohi Ud Din, M. (2020). Machine learning based approaches for detecting COVID-19 using clinical text data. International Journal of Information Technology, 12(3), 731–739. https://doi.org/10.1007/s41870-020-00495-9
[17] Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134–142. https://doi.org/10.1016/j.eswa.2015.12.030
[18] Samee, N., El-Kenawy, E.-S., Atteia, G., Jamjoom, M., Ibrahim, A., Abdelhamid, A., El-Attar, N., Gaber, T., Slowik, A., & Shams, M. (2022). Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images. Computers, Materials & Continua, 73(2), 4193–4210. https://doi.org/10.32604/cmc.2022.031147
[19] Verdonck, T., Baesens, B., Óskarsdóttir, M., & vanden Broucke, S. (2021). Special issue on feature engineering editorial. Machine Learning. https://doi.org/10.1007/s10994-021-06042-2
[20] Cerqueira, V., Moniz, N., & Soares, C. (2021). VEST: Automatic feature engineering for forecasting. Machine Learning. https://doi.org/10.1007/s10994-021-05959-y
Cite This Article
Choose your preferred format