Customer Engagement Prediction in Retail Banking with Explainable AI
DOI:
https://doi.org/10.47672/ijbs.2653Keywords:
Explainable AI, Machine Learning, Ensemble Learning, Retail BankingAbstract
Purpose: In the highly competitive retail banking environment, accurately predicting customer engagement is critical to enhancing customer satisfaction, ensuring retention, and ultimately boosting profitability.
Materials and Methods: This study embarks on a comprehensive exploration of advanced computational finance techniques by integrating Explainable Ensemble Learning (EEL) with a suite of Trustworthy Open AI tools. Utilizing state-of-the-art methods including Evidently AI for rigorous testing and LIME for interpretability this research leverages the publicly available Berka dataset.
Findings: After an intensive phase of feature engineering, extraction, and deep clustering, customers are segmented into distinct engagement categories. Four ensemble models are constructed and meticulously evaluated, with the blending model emerging as the most effective approach by achieving an impeccable AUC score of 1.000 along with outstanding accuracy, precision, recall, and F1 scores.
Implications to Theory, Practice and Policy: Only six out of 2000 data points were misclassified, underscoring the model’s robustness. This paper not only highlights significant advancements in using computational finance techniques to predict customer engagement but also emphasizes the crucial role of transparency and interpretability in fostering trust within AI-based decision systems.
Downloads
References
Aaleya Rasool, Farooq Ahmad Shah, and Muhammad Tanveer. 2021. Relational dynamics between customer engagement, brand experience, and customer loyalty: An empirical investigation. Journal of Internet Commerce 20, 3 (2021), 273–292.
Anna Omarini. 2022. Retail Banks’ Challenges and Opportunities from Vision and Strategy to Managing People, Processes and Capital. Current Aspects in Business, Economics and Finance Vol. 3 (2022), 74–103.
Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023. Trustworthy ai: From principles to practices. Comput. Surveys 55, 9 (2023), 1–46.
Dhruti Dheda, Ling Cheng, and Adnan M Abu-Mahfouz. 2022. Long short term memory water quality predictive model discrepancy mitigation through genetic algorithm optimisation and ensemble modeling. IEEE Access 10 (2022), 24638– 24658.
Diego Monferrer, Miguel Angel Moliner, and Marta Estrada. 2019. Increasing customer loyalty through customer engagement in the retail banking industry. Spanish Journal of Marketing-ESIC 23, 3 (2019), 461–484.
Forbes Makudza. 2020. Augmenting customer loyalty through customer experience management in the banking industry. Journal of Asian Business and Economic Studies 28, 3 (2020), 191–203.
Ggaliwango Marvin and Md. Golam Rabiul Alarm. 2021. An Explainable Lattice based Fertility Treatment Outcome Prediction Model for TeleFertility. In 2021 IEEE International Conference on Biomedical Engineering, Computer and Infor- mation Technology for Health (BECITHCON). 64–68. https://doi.org/10.1109/ BECITHCON54710.2021.9893623
Ibomoiye Domor Mienye and Yanxia Sun. 2022. A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access 10 (2022), 99129– 99149.
Joyee Chatterjee. 2018. Role of Services Marketing Mix and Customer-Company Identification in building Engaged Customers. Amity Journal of Management Research 3, 1 (2018), 112–122.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.
Mudasir A Ganaie, Minghui Hu, AK Malik, M Tanveer, and PN Suganthan. 2022. Ensemble deep learning: A review. Engineering Applications of Artificial Intelli- gence 115 (2022), 105151.
Neena Sondhi, Baldev R Sharma, and Supriya M Kalla. 2017. Customer engagement in the Indian retail banking sector: an exploratory study. International Journal of Business Innovation and Research 12, 1 (2017), 41–61.
Nguyen, Q. G., Nguyen, L. H., Hosen, M. M., Rasel, M., Shorna, J. F., Mia, M. S., & Khan, S. I. (2025). Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction. The American Journal of Applied sciences, 7(01), 21-30.
Nhi NY Vo, Shaowu Liu, Xitong Li, and Guandong Xu. 2021. Leveraging unstructured call log data for customer churn prediction. Knowledge-Based Systems 212 (2021), 106586
Petr Berka et al. 2000. Guide to the financial data set. PKDD2000 discovery challenge (2000).
Philipp ‘Phil’ Klaus and Stan Maklan. 2013. Towards a better measure of customer experience. International journal of market research 55, 2 (2013), 227–246.
R Salah Khairy, A Hussein, and HTHS ALRikabi. 2021. The detection of counterfeit banknotes using ensemble learning techniques of AdaBoost and voting. International Journal of Intelligent Engineering and Systems 14, 1 (2021), 326–339.
Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, et al. 2023. Explainable AI (XAI): Core ideas, techniques, and solutions. Comput. Surveys 55, 9 (2023), 1–33.
Ruiting Hao, Xiaoqian Xia, Siyi Shen, and Xiaorong Yang. 2020. Bank Direct Marketing Analysis Based on Ensemble Learning. Journal of Physics: Conference Series 1627, 1 (aug 2020), 012026. https://doi.org/10.1088/1742-6596/1627/1/
Soumi De, P Prabu, and Joy Paulose. 2021. Effective ML techniques to predict customer churn. In 2021 Third international conference on inventive research in computing applications (ICIRCA). IEEE, 895–902.
Sudi Murindanyi, Ben Wycliff Mugalu, Joyce Nakatumba-Nabende, and Ggaliwango Marvin. 2023. Interpretable Machine Learning for Predicting Customer Churn in Retail Banking. In 2023 7th International Conference on Trends in Elec- tronics and Informatics (ICOEI). 967–974. https://doi.org/10.1109/ICOEI56765. 2023.10125859
Teresa Fernandes and Teresa Pinto. 2019. Relationship quality determinants and outcomes in retail banking services: The role of customer experience. Journal of Retailing and Consumer Services 50 (2019), 30–41.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Tuan Ngoc Nguyen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
