Journal of Primeasia

Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Journal of Primeasia

Volume 6 Number 1 2025

Article Contents

Figures and Tables
RESEARCH ARTICLE   (Open Access)
Contents Vol 6 (1)

Responsible AI Governance in Banking: An Empirical Study of Fraud Detection, Privacy Protection, and Customer Trust Among US Practitioners

Md. Nazmul Haque1*, Afsara Tasnim Shama2

+ Author Affiliations

Journal of Primeasia 6 (1) 1-8 https://doi.org/10.25163/primeasia.6110903

Submitted: 29 August 2025 Revised: 09 October 2025  Published: 17 October 2025 

Abstract

Background: Banks are leaning ever more heavily on artificial intelligence to catch fraud, protect data, and speed up decisions, and that expanding footprint has, understandably, stirred concern about privacy, transparency, and whether governance can keep pace with what the technology can now do. Comparatively little empirical work asks the people actually working inside these systems what they believe. This study examines how AI-driven fraud detection and privacy protection relate to customer trust within a responsible-governance framework, drawing on the judgment of banking, fintech, and information-technology professionals in the United States.

Methods: A cross-sectional online survey was distributed to banking, fintech, IT, and financial-services professionals in the United States; 165 of 180 distributed questionnaires were usable, a 91.7% response rate. Items covering AI-powered fraud detection, privacy protection, responsible governance, and customer trust were rated on a five-point Likert scale and analyzed descriptively, then examined using Pearson correlation, multicollinearity diagnostics, and multiple linear regression in IBM SPSS Statistics v29.

Results: Perceptions were broadly favorable: 76.8% of respondents felt AI detects fraud effectively, and 72.1% felt personal information is well protected. Customer trust correlated most strongly with privacy protection (r = .756) and fraud detection (r = .721), while privacy risk was the strongest negative correlate (r = -.603). Multicollinearity diagnostics (tolerance 0.592-0.721; VIF 1.387-1.689; condition index 6.94-12.14) confirmed the predictors were sufficiently independent to support the regression model.

Conclusion: Responsible governance appears to function less as a compliance checkbox and more as the mechanism through which fraud detection and privacy protection translate into customer confidence — a relationship banks, technology vendors, and regulators would do well to take seriously as AI adoption in banking deepens.

Keywords: artificial intelligence; responsible AI governance; fraud detection; privacy protection; customer trust

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