Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers
DOI:
https://doi.org/10.37385/jaets.v6i1.5974Keywords:
Business Intelligence, Bank Marketing, Classification, Potential Deposits Customers, Ensemble Least Square Support Vector MachineAbstract
The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intelligence (BI) technology offers a strategic advantage by enabling the classification and analysis of customer data, particularly for time deposit customers. This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. Notably, the ELS-SVM achieved an average performance improvement of 10.04% over standard Support Vector Machine (SVM) techniques.
Downloads
References
Ajah, I., computing, H. N.-B. data and cognitive, & 2019, undefined. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Mdpi.Com. https://doi.org/10.3390/bdcc3020032
Aziz, F, Lawi, A., & Budiman, E. (2019). Increasing Accuracy of Ensemble Logistics Regression Classifier by Estimating the Newton Raphson Parameter in Credit Scoring. Ieeexplore.Ieee.Org. https://doi.org/10.1109/CAIPT.2017.8320700
Aziz, Firman. (2020). Klasifikasi Pelanggan Deposito Potensial menggunakan Ensembel Least Square Support Vector Machine. Journal of System and Computer Engineering, 1(1), 1. http://journal.unpacti.ac.id/index.php/JSCE/article/view/80
Busalim, A., & Society, F. G. (2021). Customer engagement behaviour on social commerce platforms: An empirical study. Elsevier. https://www.sciencedirect.com/science/article/pii/S0160791X19307481
Chan-Olmsted, S. M. (2019). A Review of Artificial Intelligence Adoptions in the Media Industry. JMM International Journal on Media Management, 21(3–4), 193–215. https://doi.org/10.1080/14241277.2019.1695619
Che, J., Zhao, S., & Li, Y. (2020). Bank telemarketing forecasting model based on t-SNE-SVM. Scirp.Org. https://www.scirp.org/journal/paperinformation.aspx?paperid=100260
Chen, R., Dewi, C., Huang, S., Data, R. C.-J. of B., & 2020, undefined. (2020). Selecting critical features for data classification based on machine learning methods. Springer, 7(1). https://doi.org/10.1186/s40537-020-00327-4
Dogra, V., Verma, S., Chatterjee, P., Shafi, J., Choi, J., & Fazal Ijaz, M. (2022). A Complete Process of Text Classification System Using State?of?the?Art NLP Models. Wiley Online Library, 2022. https://doi.org/10.1155/2022/1883698
Grzonka, D., Suchacka, G., in, B. B.-I. S., & 2016, undefined. (2016). Application of selected supervised classification methods to bank marketing campaign. Yadda.Icm.Edu.Pl, 5(1), 36–48. https://yadda.icm.edu.pl/yadda/element/bwmeta1.element.desklight-3c731462-b2de-4c6d-9e43-95ccf418785f
Heddam, S., Ptak, M., Sojka, M., Kim, S., Malik, A., Kisi, O., & Zounemat-Kermani, M. (2022). Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling. Environmental Science and Pollution Research, 29(47), 71555–71582. https://doi.org/10.1007/S11356-022-20953-0
Huji?, N., & Salihi?, F. (2020). Marketing in tourism-direct marketing as marketing communications technology. https://www.ceeol.com/search/article-detail?id=871923
Ibrahim, D., Elshennawy, N., & And, A. S. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Elsevier. https://www.sciencedirect.com/science/article/pii/S0010482521001426
Kapoor, R., & Kapoor, K. (2021). The transition from traditional to digital marketing: a study of the evolution of e-marketing in the Indian hotel industry. Worldwide Hospitality and Tourism Themes, 13(2), 199–213. https://doi.org/10.1108/WHATT-10-2020-0124/FULL/HTML
Kreituss, I., Vasiljeva, T., & Rokjane, B. (2021). Factors Important for Banks in Attracting and Retaining Customers. Lecture Notes in Networks and Systems, 195, 691–702. https://doi.org/10.1007/978-3-030-68476-1_64
Lawi, A., Aziz, F., & Syarif, S. (2018). Ensemble GradientBoost for increasing classification accuracy of credit scoring. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017, 2018-January, 1–4. https://doi.org/10.1109/CAIPT.2017.8320700
Lawi, A., Velayaty, A. A., & Zainuddin, Z. (2018). On Identifying Potential Direct Marketing Consumers using Adaptive Boosted Support Vector Machine. In K. Bali (Ed.), Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017 (pp. 1–4). https://doi.org/10.1109/CAIPT.2017.8320691
Lin, X., Featherman, M., Brooks, S. L., & Hajli, N. (2019). Exploring Gender Differences in Online Consumer Purchase Decision Making: An Online Product Presentation Perspective. Information Systems Frontiers, 21(5), 1187–1201. https://doi.org/10.1007/S10796-018-9831-1
Mujahid, M., K?na, E. R. O. L., Rustam, F., Villar, M. G., Alvarado, E. S., De La Torre Diez, I., & Ashraf, I. (2024). Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. Journal of Big Data, 11(1). https://doi.org/10.1186/S40537-024-00943-4
Mutoi Siregar, A., Faisal, S., Handayani, H. H., & Jalaludin, A. (2020). Classification Data for Direct Marketing using Deep Learning. Scientific Jounal of PPI-UKM, 7(2). https://doi.org/10.27512/sjppi-ukm/se/a15052020
Omoregie, O. K., Addae, J. A., Coffie, S., Ampong, G. O. A., & Ofori, K. S. (2019). Factors influencing consumer loyalty: evidence from the Ghanaian retail banking industry. International Journal of Bank Marketing, 37(3), 798–820. https://doi.org/10.1108/IJBM-04-2018-0099/FULL/HTML
Orjatsalo, J., Hussinki, H., & Stoklasa, J. (2024). Business analytics in managerial decision-making: top management perceptions. Measuring Business Excellence. https://doi.org/10.1108/MBE-09-2023-0130/FULL/HTML
Parlar, T., & SK, A. (2017). Using data mining techniques for detecting the important features of the bank direct marketing data. International Journal of Economics and Financial Issues, 7(2), 692–696. https://dergipark.org.tr/en/pub/ijefi/issue/32035/354551?publisher=http-www-cag-edu-tr-ilhan-ozturk
Pisner, D., & Learning, D. S. (2020). Support vector machine. Elsevier. https://www.sciencedirect.com/science/article/pii/B9780128157398000067
Quayson, A., Issau, K., Gnankob, R. I., & Seidu, S. (2024). Marketing communications’ dimensions and brand loyalty in the banking sector. Revista de Gestao, 31(1), 115–132. https://doi.org/10.1108/REGE-10-2021-0191/FULL/HTML
Rhay Vicerra, R. P., James Loresco, P., Dadios, E. P., James Loresco, P. M., & Rhay PVicerra, R. (2019). Segmentation of lettuce plants using super pixels and thresholding methods in smart farm hydroponics setup. Researchgate.Net, 12. https://www.researchgate.net/profile/Pocholo-Loresco-2/publication/334289031_Segmentation_of_Lettuce_Plants_Using_Super_Pixels_and_Thresholding_Methods_in_Smart_Farm_Hydroponics_Setup/links/5d2850e3a6fdcc2462d6b4d4/Segmentation-of-Lettuce-Plants-Using-Sup
Ruangthong, P., & S, J. (2015). Bank direct marketing analysis of asymmetric information based on machine learning. Ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/7219777/
Selma, M. (2020). Predicting the success of bank telemarketing using Artificial Neural Network. International Journal of Economics. https://publications.waset.org/10010974/predicting-the-success-of-bank-telemarketing-using-artificial-neural-network
Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised Classification Algorithms in Machine Learning: A Survey and Review. Advances in Intelligent Systems and Computing, 937, 99–111. https://doi.org/10.1007/978-981-13-7403-6_11
Shi, Y. (2022). Support Vector Machine Classification. Advances in Big Data Analytics, 97–246. https://doi.org/10.1007/978-981-16-3607-3_3
Temeng, V. A., Arthur, C. K., & Ziggah, Y. Y. (2022). Suitability assessment of different vector machine regression techniques for blast-induced ground vibration prediction in Ghana. Modeling Earth Systems and Environment, 8(1), 897–909. https://doi.org/10.1007/S40808-021-01129-0
Tharwat, A. (2021). Classification assessment methods. Emerald.Com. https://www.emerald.com/insight/content/doi/10.1016/j.aci.2018.08.003/full/html
Zhuang, Q., & Yao, Y. (2018). Application of data mining in term deposit marketing. Iaeng.Org. http://www.iaeng.org/publication/IMECS2018/IMECS2018_pp707-710.pdf