Abbasimehr, H. & Shabani, M. (2021). A new methodology for customer behavior analysis using time series clustering A case study on a bank's customers.
Kybernetes,
50(2), 221-242.
https://doi.org/10.1108/k-09-2018-0506
Aghaei, M. (2021). Market Segmentation in the Banking Industry Based on Customers’ Expected Benefits: A Study of Shahr Bank. Iranian Journal of Management Studies (IJMS) 14(3), 629-648.
Al-Wugayan, A. A. A. (2019). Relationship versus customer experience quality as determinants of relationship quality and relational outcomes for Kuwaiti retail banks.
International Journal of Bank Marketing,
37(5), 1234-1252.
https://doi.org/10.1108/ijbm-09-2018-0251
Ala'raj, M. & Abbod, M. F. (2016). Classifiers consensus system approach for credit scoring.
Knowledge-Based Systems,
104, 89-105.
https://doi.org/10.1016/j.knosys.2016.04.013
Ala'raj, M., Abbod, M. F. & Majdalawieh, M. (2021). Modelling customers credit card behaviour using bidirectional LSTM neural networks.
Journal of Big Data,
8(1).
https://doi.org/10.1186/s40537-021-00461-7
Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J. & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty.
Journal of Business Research,
94, 290-301.
https://doi.org/https://doi.org/10.1016/j.jbusres.2018.03.003
Andaleeb, S. S., Rashid, M. & Rahman, Q. A. (2016). A model of customer-centric banking practices for corporate clients in Bangladesh. International Journal of Bank Marketing, 34(4), 458-475. doi:10.1108/ijbm-10-2014-0156
Ashofteh, A. & Bravo, J. M. (2021). A conservative approach for online credit scoring.
Expert Systems with Applications,
176, 16.
https://doi.org/10.1016/j.eswa.2021.114835
Baghani, E., Elahi, S., Hasanzadeh, A. & Rajabzadeh Ghatari, A. (2023). Taxonomy of Customers Identification in Banking Industry Using Machine Learning: a Systematic Review with a Meta-Synthesis Approach. Iranian Journal of Information Processing and Management, 39(1), 394-429.
Bharathi, S. V., Pramod, D. & Raman, R. (2022). An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers.
DATA,
7(5).
https://doi.org/10.3390/data7050061
Boustani, N., Emrouznejad, A., Gholami, R., Despic, O. & Ioannou, A. (2024). Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks. Annals of Operations Research, 339(1), 613-630. https://doi.org/10.1007/s10479-023-05209-5
Brinkmann, S. & Kvale, S. (2014). InterViews (Learning the Craft of Qualitative Research Interviewing). Sage.
Calvo-Porral, C. & Levy-Mangin, J. P. (2020). An emotion-based segmentation of bank service customers.
International Journal of Bank Marketing,
38(7), 1441-1463.
https://doi.org/10.1108/ijbm-05-2020-0285
Chang, V., Sivakulasingam, S., Wang, H., Wong, S. T., Ganatra, M. A. & Luo, J. (2024). Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers. Risks, 12(11), 174. https://doi.org/10.3390/risks12110174
Chen, T. H. (2020). Do you know your customer? Bank risk assessment based on machine learning.
Applied Soft Computing,
86.
https://doi.org/10.1016/j.asoc.2019.105779
Clarke, V. & Braun, V. (2016). Thematic Analysis. The Journal of Positive Psychology, 12, 297-298. https://doi.org/10.1080/17439760.2016.1262613
Creswell, J. W. & Clark, V. L. P. (2017).
Designing and Conducting Mixed Methods Research. SAGE Publications.
https://books.google.com.hk/books?id=A39ZDwAAQBAJ
Dawood, E. A., Elfakhrany, E. & Maghraby, F. A. (2019). Improve Profiling Bank Customer's Behavior Using Machine Learning.
Ieee Access,
7, 109320-109327.
https://doi.org/10.1109/access.2019.2934644
De Lima Lemos, R. A., Silva, T. C. & Tabak, B. M. (2022). Propension to customer churn in a financial institution: a machine learning approach.
Neural Computing and Applications,
34(14), 11751-11768.
https://doi.org/10.1007/s00521-022-07067-x
Dehnert, M. & Schumann, J. (2022). Uncovering the digitalization impact on consumer decision-making for checking accounts in banking.
Electronic Markets,
32(3), 1503-1528.
https://doi.org/10.1007/s12525-022-00524-4
Eltahir, A.M., Mohamed Ahmed, T., Abdelmageed Mohammed, A.A., Mohamedsalih Hilal, A.M. & Abdalfadil, T. A. (2022). An Approach of Supervised and Unsupervised Machine Learning Model for E-CRM Bank’s Marketing. International Journal of Computer Science and Network Security, 22(4), 625-636.
Estrella-Ramon, A., Sanchez-Perez, M., Swinnen, G. & VanHoof, K. (2017). A model to improve management of banking customers.
Industrial Management & Data Systems,
117(2), 250-266.
https://doi.org/10.1108/imds-03-2016-0107
Grassi, L., Figini, N. & Fedeli, L. (2022). How does a data strategy enable customer value? The case of FinTechs and traditional banks under the open finance framework.
Financial Innovation,
8(1), Article 75.
https://doi.org/10.1186/s40854-022-00378-x
Izogo, E. E., Jayawardhena, C. & Kalu, A. O. U. (2018). Examining customers' experience with the Nigerian Bank Verification Number (BVN) policy from the perspective of a dual-lens theory.
International Journal of Emerging Markets,
13(4), 709-730.
https://doi.org/10.1108/IJoEM-09-2016-0246
Jain, H., Yadav, G. & Manoov, R. (2020). Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques. In Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019 (pp. 137-156). Singapore: Springer Singapore.
Keiningham, T., Aksoy, L., Bruce, H. L., Cadet, F., Clennell, N., Hodgkinson, I. R. & Kearney, T. (2020). Customer experience driven business model innovation.
Journal of Business Research,
116, 431-440.
https://doi.org/10.1016/j.jbusres.2019.08.003
Khalili, N. & Rastegar, M. A. (2023). Optimal cost-sensitive credit scoring using a new hybrid performance metric.
Expert Systems with Applications,
213, Article 119232.
https://doi.org/10.1016/j.eswa.2022.119232
Kidron, A. & Kreis, Y. (2020). Listening to bank customers: the meaning of trust.
International Journal of Quality and Service Sciences,
12(3), 355-370.
https://doi.org/10.1108/ijqss-10-2019-0120
Kovács, T., Ko, A. & Asemi, A. (2021). Exploration of the investment patterns of potential retail banking customers using two-stage cluster analysis.
Journal of Big Data,
8(1), 141.
https://doi.org/10.1186/s40537-021-00529-4
Li, L., Wang, J. & Li, X. (2020). Efficiency Analysis of Machine Learning Intelligent Investment Based on K-Means Algorithm.
Ieee Access,
8, 147463-147470.
https://doi.org/10.1109/access.2020.3011366
Liu, Y., Yang, M., Wang, Y., Li, Y., Xiong, T. & Li, A. (2022). Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China.
International Review of Financial Analysis,
79, 101971.
https://doi.org/https://doi.org/10.1016/j.irfa.2021.101971
Livieris, I. E., Kiriakidou, N., Kanavos, A., Tampakas, V. & Pintelas, P. (2018). On ensemble SSL algorithms for credit scoring problem .
Informatics,
5(4), Article 40.
https://doi.org/10.3390/informatics5040040
Mancisidor, R. A., Kampffmeyer, M., Aas, K. & Jenssen, R. (2021b). Learning latent representations of bank customers with the Variational Autoencoder.
Expert Systems with Applications,
164, 13, Article 114020.
https://doi.org/10.1016/j.eswa.2020.114020
Mestiri, S. (2024). Credit scoring using machine learning and deep Learning-Based models.
4(2), 236-248.
https://doi.org/10.3934/DSFE.2024009
Mosavi, A. B. & Afsar, A. (2018). Customer Value Analysis in Banks Using Data Mining and Fuzzy Analytic Hierarchy Processes.
International Journal of Information Technology & Decision Making,
17(3), 819-840.
https://doi.org/10.1142/s0219622018500104
Pandey, M. K., Mittal, M. & Subbiah, K. (2021). Optimal balancing & efficient feature ranking approach to minimize credit risk.
International Journal of Information Management Data Insights,
1(2), Article 100037.
https://doi.org/10.1016/j.jjimei.2021.100037
Pawełoszek, I. (2021). Customer segmentation based on activity monitoring applications for the recommendation system.
Procedia Computer Science,
192, 4751-4761.
https://doi.org/https://doi.org/10.1016/j.procs.2021.09.253
Pine, B. J. & Gilmore, J. H. (1998). Welcome to the Experience Economy. Harvard Business Review, 97-105.
Plawiak, P., Abdar, M., Plawiak, J., Makarenkov, V. & Acharya, U. R. (2020). DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring.
Information Sciences,
516, 401-418.
https://doi.org/10.1016/j.ins.2019.12.045
Plessis, L. & Vries, M. (2016). Towards a holistic customer experience management framework for enterprises.
South African Journal of Industrial Engineering,
27, 23-36.
https://doi.org/10.7166/27-3-1624
Scott, E., Milani, F., Kilu, E. & Pfahl, D. (2021). Enhancing agile software development in the banking sector-A comprehensive case study at LHV.
Journal of Software-Evolution and Process,
33(7), Article e2363.
https://doi.org/10.1002/smr.2363
Song, X., Liu, M. T., Liu, Q. & Niu, B. (2021). Hydrological cycling optimization‐based multiobjective feature‐selection method for customer segmentation.
International Journal of Intelligent Systems,
36(5), 2347-2366.
https://doi.org/10.1002/int.22381
Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk.
Journal of internet Commerce,
18(1), 91-111.
https://doi.org/10.1080/15332861.2019.1567188
Urkup, C., Bozkaya, B. & Salman, F. S. (2018). Customer mobility signatures and financial indicators as predictors in product recommendation.
Plos One,
13(7), 18, Article e0201197.
https://doi.org/10.1371/journal.pone.0201197
Yuan, K., Chi, G., Zhou, Y. & Yin, H. (2022). A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description. Research in International Business and Finance, 59, 101536.