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Paper ID:IJSREM67957

Determinants of Customer Adoption of Generative AI-Enabled Banking Services in India: An Empirical Study

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67957

Volume:

10 |

10 |

Oct 2026

Pages:

1-13

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67957

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

GRANDHI JAYALAKSHMI

GRANDHI JAYALAKSHMI

BACHELOR OF BUSINESS ADMINISTRATION

KONERU LAKSHMAIAH EDUCATION FOUNDATION

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DSR

DR.MEDA SRINIVASA RAO

BACHELOR OF BUSINESS ADMINISTRATION

KONERU LAKSHMAIAH EDUCATION FOUNDATION

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Abstract

While GenAI is transforming the customer interface in banking, there is limited empirical evidence that offers insights into why customers in India may or may not embrace GenAI. This study explores the effects of perceived usefulness (PU), perceived ease of use (PEOU), security and privacy (SP) and perceived risk (PR) on customers' intentions to use GenAI capabilities in Indian banking, examines trust in GenAI as a mediator between the two, and examines differences in intention across groups of respondents. A structured online questionnaire (five-point Likert scale) was used to gather data, which was subsequently analysed in MS Excel using descriptive statistics, Cronbach's alpha, chi-square tests of independence, one-way ANOVA and regression-based mediation (Sobel test) and moderation (interaction terms) models. Together, the four perceived characteristics accounted for 34.2% of the variance in adoption intention (F(4, 126) = 16.35, p < .001); and, when examined individually, only perceived usefulness was found to be a statistically significant predictor (B = 0.226, p = .017). All four characteristics were robust predictors of trust in GenAI (R² = .739) but failed to mediate the influence of the four on the adoption intention (all Sobel p > .42). There were four significant moderation effects (interaction p < .05) for digital literacy. In the chi-square tests, intention was not significantly different across gender or prior use or frequency of use of the AI features, and in the ANOVA, intention was significantly different across usage frequencies (F(4, 126) = 4.68, p = .001), and was linked to age group. The study provides a preliminary evidence basis for banks and policymakers in India around the adoption of GenAI by customers.

KEYWORDS

Generative AI; GenAI-enabled banking; customer adoption intention; perceived usefulness; perceived risk; trust; digital literacy; India

Cite This Article

GRANDHI JAYALAKSHMI, DR.MEDA SRINIVASA RAO, "Determinants of Customer Adoption of Generative AI-Enabled Banking Services in India: An Empirical Study," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-13, Oct 2026. https://doi.org/10.55041/IJSREM67957

Full Article/Conclusion

This research explores the factors influencing customers' decision to embrace GenAI-powered banking products in India. Taken together, perceived usefulness, ease of use, security and privacy and perceived risk accounted for nearly one-third of the variance in adoption intention, with perceived usefulness being the primary individual predictor for the overall sample. These did not go through trust, and digital literacy significantly moderated all four relations. Prior use of AI-features and intention were largely independent of each other and gender. It is important to note that the data are numerous and repeated responses, and are obtained from a student sample, so should be viewed as tentative evidence, subject to further investigation with a larger, verified and more diverse sample.

References

1. Lee, D. K. C., Guan, C., Yu, Y., & Ding, Q. (2024). A comprehensive review of generative AI in finance. FinTech, 3(3), 460–478. https://doi.org/10.3390/fintech3030025 2. Thuraisamy, K. S., Baig, Z., & Zeadally, S. (2025). Generative artificial intelligence in financial services: Opportunities, challenges, and cyberthreats. IT Professional, 27(2), 35–42. https://doi.org/10.1109/MITP.2025.3534270 3. Moharrak, M., & Mogaji, E. (2025). Generative AI in banking: Empirical insights on integration, challenges and opportunities in a regulated industry. International Journal of Bank Marketing, 43(4), 871–896. https://doi.org/10.1108/IJBM-08-2024-0490 4. Chua, X. J., Tan, J. M. L., Tan, J. X., Poh, S. C., Goh, Y. X., Choong, D. H. T., Foong, C. M., Yang, S. J., & Chan, C. S. (2025). Banking done right: Redefining retail banking with language-centric AI. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, 646–658. https://doi.org/10.18653/v1/2025.emnlp-industry.45 5. Chen, Y., Yan, S., Jin, Y., Li, J., & Jin, X. (2025). Generative artificial intelligence in finance: A systematic literature review and a research agenda. Digital Technologies Research and Applications, 4(2), 14–32. https://doi.org/10.54963/dtra.v4i2.1191 6. Raju, R. (2025). From models to markets: Generative AI and its emerging role in Indian financial services. SSRN. https://doi.org/10.2139/ssrn.5223947 7. Fan, M. (2024). LLMs in banking: Applications, challenges and approaches. In Proceedings of the International Conference on Digital Economy, Blockchain and Artificial Intelligence (DEBAI 2024). Association for Computing Machinery. https://doi.org/10.1145/3700058.3700107 8. Ek, L., & Arnarp, E. (2024). Generative AI in traditional banking institutes [Master’s thesis, Lund University]. Lund University Publications. Lund University Publications 9. Gehrmann, S., Huang, C., Teng, X., Yurovski, S., Bhorkar, A., Thomas, N., Doucette, J., Rosenberg, D., Dredze, M., & Rabinowitz, D. (2025). Understanding and mitigating risks of generative AI in financial services. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25). Association for Computing Machinery. https://doi.org/10.1145/3715275.3732168 10. Ostrowska, A. (2025). Large language models in the banking sector: Areas, opportunities, and threats. Ruch Prawniczy, Ekonomiczny i Socjologiczny, 87(3), 277–298. https://doi.org/10.14746/rpeis.2025.87.3.16

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Publication Date

10/14/2026

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