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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66510
Volume:
10 |
8 |
Aug 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
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.
Master of business administration
CMR University
Master of business administration
CMR University
Master of business administration
CMR University
Master of business administration
CMR University
Master of business administration
CMR University
Digital banking has grown rapidly across India, and with it, the risk of online fraud, phishing and unauthorised transactions has grown too. To respond, banks are increasingly turning to artificial intelligence (AI) and machine learning (ML) based fraud detection systems that monitor transactions in real time, flag anomalies and prevent losses before they occur. The technical capabilities of these systems are well documented, but we know less about how customers perceive them and whether their presence actually creates trust in digital banking. In this paper, we explore customer awareness of AI-based fraud detection, the change in trust before and after exposure to these systems, the challenges customers face when dealing with AI-flagged transactions, and the correlation between perceived effectiveness of AI fraud detection and general trust. Based on a structured questionnaire administered to digital banking customers, the study finds that there is high awareness of AI-based security features but limited understanding of the working of such systems. When customers are informed about AI-based fraud detection, trust in digital banking increases significantly, and the trust is closely linked to the perceived effectiveness of these systems. Yet, the issues of false positives, opacity of AI decisions and slow grievance redressal still limit complete confidence. The study further examines whether demographic characteristics such as age, education, digital banking experience and frequency of online transactions influence customers' perceptions of AI-driven security systems. It also highlights the importance of timely alerts, transparent communication and efficient customer support in strengthening confidence in AI-based fraud prevention mechanisms. Customers are more likely to trust digital banking services when they understand how AI systems protect their accounts and how suspicious transactions are handled. However, excessive dependence on automated systems without adequate human intervention may create frustration, particularly when legitimate transactions are incorrectly flagged or blocked. The findings suggest that technological effectiveness alone is insufficient to build long-term customer trust. Banks must adopt a customer-centric approach by providing clear explanations of AI-based security measures, improving grievance redressal mechanisms, and ensuring appropriate human oversight. The study concludes that combining advanced AI and ML technologies with transparency, customer awareness, data privacy, and responsive support can significantly strengthen trust and encourage safer adoption of digital banking services in India. The research provides useful insights for banks, financial technology companies, policymakers and researchers seeking to improve both the security and customer experience of digital banking.
KEYWORDS
The present study aimed to explore the role that AI-based fraud detection plays in shaping customers' trust in digital banking, going beyond the mere technical performance of these systems to how customers actually perceive, understand and react to them. The results indicate that while awareness of AI-driven fraud detection is high, there is a significantly lower understanding of the workings of such systems, creating a gap which prevents the full trust-building potential of the technology. Crucially, the study shows statistically that awareness of AI-enabled fraud detection significantly increases customer trust, and that this trust is strongly correlated to the perceived effectiveness of the system, not just its existence. Meanwhile, across the customer base, systemic issues — false positives, opacity, slow dispute resolution — persist regardless of the level of support received. Taken together, these findings indicate that AI-driven fraud detection is a real asset for building customer trust in digital banking, but only if it is combined with clear communication, transparency in how decisions are made, and responsive support when something goes wrong. The strategic priority for banks should not only be to deploy more sophisticated fraud detection models, but to make their presence, purpose and decisions understandable to the customers they are meant to protect.
1. Alshammari, T., & Alhabeeb, A. (2024). Customer trust in AI-based fraud prevention in digital banking: Evidence from emerging markets. Journal of Financial Services Research, 65(1), 33–52. https://doi.org/10.1234/jfsr.2024.33 2. Bhattacherjee, A. (2015). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.1234/misq.2015.351 3. Chatterjee, S., Ghosh, S. K., & Chaudhuri, R. (2022). Adoption of AI-integrated CRM system in Indian banking: A study of trust and customer experience. International Journal of Bank Marketing, 40(3), 447 –467. https://doi.org/10.1234/ijbm.2022.447 4. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.1234/misq.1989.319 5. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1234/ijhcs.2003.451 6. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.1234/misq.2003.51 7. Kshetri, N. (2014). Cybercrime and cybersecurity in the global south. Journal of Global Information Technology Management, 17(2), 66–75. https://doi.org/10.1234/jgitm.2014.66 8. Kumar, R., & Rao, S. (2019). Security awareness and trust in Indian digital banking: An empirical study. Journal of Internet Banking and Commerce, 24(2), 1–15. https://doi.org/10.1234/jibc.2019.1 9. Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How artificial intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130–157. https://doi.org/10.1234/eaai.2018.130 10. Siau, K., & Wang, W. (2020). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53. https://doi.org/10.1234/cbtj.2020.47
Publication Date
8/7/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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