AI vs. Humans in U.S. Retail Banking: A Pilot Study on Customer Satisfaction and Service Excellence

Authors

DOI:

https://doi.org/10.15421/cims.4.321

Keywords:

artificial intelligence, human interaction, customer satisfaction, retail banking, service excellence, hybrid AI-human models

Abstract

Purpose. This pilot study aims to explore the perceived effectiveness of artificial intelligence (AI) and human interaction models within a university-affiliated sample, focusing on U.S. retail banking customer satisfaction and service excellence. Design / Method / Approach. The pilot study involved 50 participants from a university community—U.S. retail banking customers affiliated with Wright State University. A structured survey was conducted, and data were analyzed using descriptive statistics, predictive analytics, ANOVA, regression analysis, and t-tests to assess satisfaction levels, response times, and service preferences. Findings. Results indicate that human interaction consistently outperformed AI, with an average satisfaction score of 4.14 compared to 3.56 for AI (p<0.05). Male participants rated AI higher, whereas females preferred human interaction. Regression analysis revealed that AI satisfaction was primarily influenced by service consistency (p=0.035, R²=0.176), while human satisfaction was driven by personalized service (p=0.009, R²=0.220). These results suggest that empathy and personalization remain central to service excellence, while AI’s consistency can enhance operational efficiency. Theoretical Implications. The findings contribute to understanding the human–AI service trade-off by integrating behavioral and demographic dimensions into service design. Practical Implications. The study recommends enhancing the consistency of AI-driven systems and investing in employee training programs to strengthen empathy and personalization, which together foster loyalty and customer trust. Originality / Value. This work empirically substantiates the dual path toward technological efficiency and human-centric empathy, highlighting hybrid AI–human approaches as optimal for banking economics. Research Limitations / Future Research. Limited sample size and homogeneity restrict generalization; future research should employ larger, demographically diverse samples and longitudinal designs to explore mediating factors such as trust and cultural context. Article Type. Exploratory Research / Pilot Study.

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Author Biography

  • Sifat Mahmud, Wright State University

    Holds a Master of Science in Marketing Analytics and Insights from Wright State University (USA), a master’s degree in professional banking from the University of Dhaka (Bangladesh), and a BBA in International Business from AIUB (Bangladesh). Has over 13 years of experience in banking and analytics. Contributed to academic and campus activities through roles as RSCoB Graduate Student Ambassador, Graduate Academic Researcher, Student Safety Advocate for the University Police Department, and Student Development Officer for the Annual Giving Department. Served as a plenary speaker at the Celebration of Research 2023 and was featured in university media outlets. Professional achievements include conducting strategic market research and analytical projects that supported business growth at HSBC, PUI Audio, and Boost Engagement, LLC, including managing the implementation of an international warehouse that enhanced U.S. trade operations. Fluent in English, Bengali, and Hindi. Research and personal interests include marketing analytics, customer behavior, travel, and music.

References

Ameen, N., Tarhini, A., Shah, M. H., & Nusair, K. (2021). A cross cultural study of gender differences in omnichannel retailing contexts. Journal of Retailing and Consumer Services, 58, 102265. https://doi.org/10.1016/j.jretconser.2020.102265

Calabrese, A., Costa, R., & Rosati, F. (2016). Gender differences in customer expectations and perceptions of corporate social responsibility. Journal of Cleaner Production, 116, 135–149. https://doi.org/10.1016/j.jclepro.2015.12.100

Deloitte Network. (2024). New Deloitte Survey: Increasing Consumer Privacy and Security Concerns in the Generative AI Era. Deloitte Touche Tohmatsu Limited. https://e.surl.li/xuzbdz

Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209–223. https://doi.org/10.1016/j.jretai.2021.03.001

McKinsey & Company. (2021). Building the AI bank of the future: Global Banking Practice. McKinsey & Company. https://e.surl.li/tnwpvw

Méndez-Suárez, M., Monfort, A., & Hervas-Oliver, J.-L. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), 100223. https://doi.org/10.1016/j.iedeen.2023.100223

Pattanayak, S. K. (2021). The Impact of Artificial Intelligence on Operational Efficiency in Banking: A Comprehensive Analysis of Automation and Process Optimization. International Research Journal of Engineering and Technology, 8(10), 2049–2061. https://www.irjet.net/archives/V8/i10/IRJET-V8I10315.pdf

Scheffler, P. & Puczyk, A. (2025). AI in Retail Banking: Use Cases, Challenges, and Trends. Neontri. https://neontri.com/blog/ai-retail-banking/

The Contentstack Team. (2024). Personalized experiences: How emotional intelligence improves customer experience. Contentstack Inc. https://e.surl.li/lkguqv

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Published

2025-11-26

How to Cite

Mahmud, S. (2025). AI vs. Humans in U.S. Retail Banking: A Pilot Study on Customer Satisfaction and Service Excellence. Challenges and Issues of Modern Science, 4(2), 321. https://doi.org/10.15421/cims.4.321

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