Journal of Primeasia

1. Introduction

Walk into almost any bank branch today — or, more realistically, open its app — and artificial intelligence is already quietly at work behind the interface, deciding within milliseconds whether a transaction looks like you or looks like someone pretending to be you. That shift did not happen overnight, and it has not been universally welcomed. AI has undeniably reshaped how financial institutions detect fraud, manage risk, and personalize service (Ridzuan et al., 2024), and a good deal of that reshaping has been genuinely useful: algorithms that once needed hours to flag suspicious activity can now do it in real time, narrowing what used to be a fairly generous window for fraud to slip through (Mytnyk et al., 2023). And yet the same systems that make banking faster and, arguably, safer also pull in enormous volumes of personal and financial data — and it is here, in that quiet accumulation, that unease tends to creep in (Zhao & Zhang, 2021).

It is worth pausing, briefly, on why this matters more now than it might have five or ten years ago. Machine-learning models can sift through transaction histories, spending patterns, and behavioral signals far faster than any rule-based system, catching anomalies a human analyst might otherwise miss entirely (Khaifa et al., 2021). That capability is not seriously in dispute. What remains far less settled, though, is what happens around the edges of that capability: who is accountable when a model gets it wrong, whether its reasoning can be explained to a regulator or an ordinary customer, and whether the institutions deploying these tools are doing so within some coherent ethical framework rather than simply because the technology happens to exist (Turksen et al., 2024). Responsible AI governance — a phrase that can sound almost bureaucratic until one sits with what it actually demands — asks banks to hold transparency, accountability, fairness, and security together at once, not as separate boxes to tick but as an integrated discipline (Aldboush & Ferdous, 2023).

Privacy sits close to the center of this conversation, arguably closer than fraud detection itself. Predictive, personalized banking services simply cannot function without continuous access to customers' financial and behavioral data (Basit et al., 2020; Tao et al., 2019), and that dependency creates real exposure — to breaches, to misuse, to the slow erosion of confidence that follows once people sense their information is being handled carelessly (Mutimukwe et al., 2019; Varma et al., 2022). Banks that get this wrong do not merely risk regulatory penalties; they risk something harder to rebuild, namely customers' willingness to keep sharing information at all (Lui & Lamb, 2018).

And that, in the end, is where trust enters the picture — not as an afterthought, but as the variable that arguably determines whether any of the rest of this actually works. Customers who doubt the fairness or transparency of an AI system are unlikely to embrace it, however technically sophisticated it might be (Garcia-Segura, 2024). Trust, in other words, is not merely a pleasant byproduct of good governance; it may be the mechanism through which governance translates into adoption, and adoption into the long-term data-sharing relationship banks depend on (Rahahleh et al., 2021).

Despite how central these three threads — fraud detection, privacy protection, and trust — appear to be, the literature has tended to treat them as separate conversations rather than as parts of one interconnected system (Enholm et al., 2021). Fewer studies still have asked the people actually operating inside these systems — bankers, fintech professionals, compliance staff — what they believe is happening on the ground. This study attempts to close part of that gap. Drawing on survey responses from 165 banking, fintech, information-technology, and financial-services professionals across the United States, it examines how AI-enabled fraud detection and privacy protection relate to customer trust under a responsible-governance lens, with the broader aim of offering both empirical grounding and practical direction for banks, technology vendors, and policymakers navigating this fast-moving terrain.

2. Materials and Methods

2.1 Study Design

This study used a quantitative, cross-sectional survey design to examine perceptions of AI-driven fraud detection, privacy protection, and responsible governance in relation to customer trust within the United States banking sector. A cross-sectional approach was chosen because the aim was to capture a snapshot of practitioner perceptions at a single point in time, rather than to track change — a design well suited to exploratory work on a relatively young governance topic (Aldboush & Ferdous, 2023).

2.2 Setting and Eligibility

Eligible participants were adults working in the United States within commercial banking, financial technology (fintech), information technology serving the financial sector, or academic institutions with a professional connection to digital banking, cybersecurity, or financial-technology practice. Participants needed direct working exposure to AI-enabled systems, cybersecurity practices, or digital-banking operations to be included; individuals without such exposure were excluded, since the survey asked about first-hand perceptions of AI system performance rather than general attitudes toward technology.

2.3 Sample Size and Recruitment

A purposive sampling strategy was used: rather than sampling at random from an unknown population of eligible professionals, participants were deliberately identified and invited because their roles gave them direct, informed exposure to AI systems in banking. In total, 180 surveys were distributed; 165 completed responses were retained for analysis after removing incomplete submissions, yielding a response rate of 91.7%. No formal a priori power calculation was performed; the achieved sample size is broadly consistent with comparable practitioner-perception surveys in the AI-banking literature and was judged sufficient to support the planned correlation and regression analyses, though this should be regarded as a pragmatic rather than a statistically optimized sample size, and is noted as a limitation.

2.4 Survey Instrument

The questionnaire had two parts. The first collected demographic and professional information: gender, age group, highest educational qualification, employment sector, and years of professional experience. The second part measured three constructs, each represented by six items rated on a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree):

(1) AI-Powered Fraud Detection and Prevention — perceptions of AI's ability to detect fraud, prevent fraudulent transactions, monitor activity in real time, enhance transaction security, minimize financial loss, and support risk prediction;

(2) Customer Privacy Protection and Data Security — perceptions of personal information security, data governance, customer data protection, breach prevention, data confidentiality, and regulatory compliance;

(3) Responsible AI Governance and Customer Trust — perceptions of transparency, fairness, accountability, ethical decision-making, banking confidence, and overall customer trust in AI-powered banking services.

Item wording was informed by constructs discussed in the responsible-AI and banking-governance literature (Aldboush & Ferdous, 2023; Turksen et al., 2024) and adapted to reflect banking-specific practice. For full transparency and to support replication, we recommend that the complete 18-item instrument, along with exact item wording and response anchors, be included as a supplementary appendix in the submitted version of this manuscript; it is not reproduced in full here.

2.5 Data Collection Procedure

Data were collected using a structured online questionnaire. Invitations were distributed electronically to eligible professionals, along with a short description of the study's purpose and an estimate of completion time. Participation was voluntary, and respondents could withdraw at any point before submission. Responses were collected and stored electronically; identifying information (such as email address) was not linked to individual response records in the analysis dataset. [Authors should specify here: survey platform used, data collection window (start and end dates), and any quality-control steps such as attention-check items or duplicate-response screening, to fully satisfy reproducibility expectations for indexed journals.]

2.6 Ethical Considerations

[Authors should insert the specific ethical approval statement here — e.g., the name of the Institutional Review Board or ethics committee, approval number, and date, along with a statement confirming that informed consent was obtained from all participants prior to participation, and that participation was voluntary and anonymous. This information was not present in the original manuscript draft and must be supplied by the authors before submission, as most indexed journals require it verbatim.]

2.7 Statistical Analysis

All analyses were conducted in IBM SPSS Statistics, Version 29. Descriptive statistics (frequencies and percentages) summarized demographic characteristics and item-level perception data. Pearson product-moment correlation coefficients were computed to examine the strength and direction of association among fraud detection, privacy protection, customer trust, privacy risk, algorithmic bias, and data-misuse concern. Multicollinearity among predictor variables was assessed using tolerance, Variance Inflation Factor (VIF), and

Table 1. Demographic characteristics of survey respondents (N = 165). Values are presented as frequency (n) and percentage (%) for gender, age group, highest educational qualification, employment sector, and years of professional experience among banking, fintech, information-technology, and financial-services professionals surveyed in the United States.

Table 2. Pearson correlation matrix among the six study variables (N = 165). FD = Fraud Detection; PP = Privacy Protection; CT = Customer Trust; PR = Privacy Risk; AB = Algorithmic Bias; DC = Data Misuse Concern. Values represent Pearson product-moment correlation coefficients (r); all coefficients were derived from five-point Likert-scale perception scores. Negative values indicate inverse association. FD = Fraud Detection; PP = Privacy Protection; CT = Customer Trust; PR = Privacy Risk;  AB = Algorithmic Bias; DC = Data Misuse Concern.

condition index, applying conventional thresholds (tolerance > 0.10; VIF < 5; condition index < 30) to judge acceptability. Multiple linear regression was then used to estimate the combined contribution of fraud detection and privacy protection to customer trust. Internal consistency reliability of each six-item construct (e.g., Cronbach's alpha) should be reported here in the final version; it was not available in the analysis provided and is flagged as a required addition prior to submission.

3. Results

3.1 Demographic Characteristics of Respondents

Of the 165 respondents, men made up the majority (60.0%), with women comprising 40.0% (Table 1). The largest age band was 30-39 years (35.8%), followed by 40-49 years (26.7%), suggesting a sample weighted toward mid-career professionals rather than early-career staff or senior executives. Educational attainment was notably high: roughly half held a master's degree (49.7%), and an additional 24.8% held a bachelor's degree. Most respondents worked in commercial banking (50.9%), with the remainder distributed across fintech (20.0%), information technology (17.0%), and academic institutions (12.1%). In terms of experience, 38.8% had 5-10 years in the field and 23.6% had 11-15 years — again pointing to a sample of professionals with substantial, rather than entry-level, exposure to the systems being evaluated.

3.2 AI-Powered Fraud Detection and Prevention

Perceptions of AI-based fraud detection were consistently positive across all six dimensions examined (Figure 1). Fraud detection itself drew the strongest endorsement (76.8%), followed closely by fraud prevention (74.6%) and real-time monitoring (73.9%). A further 71.5% of respondents agreed that AI strengthens transaction security. Interestingly, the two dimensions tied most closely to downstream financial outcomes — minimizing financial loss (68.7%) and risk prediction (66.2%) — scored comparatively lower, even though they remained above two-thirds agreement. This pattern hints that practitioners may find it easier to credit AI with detecting a problem than with fully resolving its financial consequences.

3.3 Customer Privacy Protection and Data Security

Ratings of AI-driven privacy protection were likewise favorable across indicators (Figure 2). Personal information security received the highest positive rating (72.1%), followed by data governance (71.4%) and customer data security (70.9%). Just under 70% of respondents felt AI helps guard against data breaches (69.3%) and supports data confidentiality (68.4%). Compliance with privacy regulations drew the lowest, though still majority-level, positive rating (66.7%), alongside the highest neutral response of any privacy item (20.3%). Negative ratings throughout this construct remained modest, ranging from 11.5% to 13.0%, suggesting that skepticism, where it existed, tended toward uncertainty rather than active distrust.

3.4 Responsible AI Governance and Customer Trust

Attitudes toward AI governance were positive across every factor considered (Figure 3). Among "Agree" responses, fairness in AI use was endorsed most (42.4%), followed by transparency (41.8%), banking confidence (40.6%), and customer trust itself (40.0%). Among "Strongly Agree" responses, ethical decision-making led (32.1%), followed by customer trust (31.5%), banking confidence (30.9%), and accountability (30.3%). Neutral responses clustered between 18.2% and 20.0%, and disagreement remained low throughout, from 9.1% to 10.3% — a pattern that, taken together, suggests practitioners view governance qualities less as abstract ideals and more as practical prerequisites for trustworthy AI banking.

3.5 Correlations Among Study Variables

Pearson correlations among the six study variables are reported in Table 2. Customer trust correlated most strongly with privacy protection (r = .756) and, close behind, with fraud detection (r = .721), indicating that both dimensions move closely together with practitioners' confidence in AI-based banking. Fraud detection and privacy protection were themselves moderately correlated (r = .674). On the other side of the ledger, privacy risk was negatively associated with customer trust (r = -.603), privacy protection (r = -.521), and fraud detection (r = -.384). Algorithmic bias and data-misuse concern followed a similar negative pattern with respect to trust, privacy protection, and fraud detection, while correlating positively with privacy risk (r = .612) and with each other (r = .648) — a coherent pattern suggesting these three "concern" variables may reflect a shared underlying skepticism.

3.6 Multicollinearity Diagnostics

Multicollinearity diagnostics for the predictor variables are shown in Table 3. Tolerance values ranged from 0.592

Table 3. Multicollinearity diagnostics for predictor variables entered into the regression model (N = 165). Tolerance, Variance Inflation Factor (VIF), and Condition Index are reported for each variable; conventional thresholds (tolerance > 0.10, VIF < 5, condition index < 30) were applied to evaluate the absence of problematic multicollinearity. FD = Fraud Detection; PP = Privacy Protection; CT = Customer Trust; PR = Privacy Risk; AB = Algorithmic Bias; DC = Data Misuse Concern.

Figure 1. Respondent perceptions of AI-powered fraud detection and prevention (N = 165). Bars represent the percentage of respondents endorsing each dimension — fraud detection, fraud prevention, real-time monitoring, transaction security, minimization of financial loss, and risk prediction — on a five-point Likert scale, collapsed into positive, neutral, and negative response categories.

(customer trust) to 0.721 (data-misuse concern), and VIF values ranged from 1.387 to 1.689, with condition indices spanning 6.94 to 12.14. All values fell comfortably within conventional thresholds (tolerance > 0.10; VIF < 5; condition index < 30), indicating that multicollinearity was not a meaningful threat to the reliability of the regression model.

4. Discussion

Taken as a whole, these findings paint a picture of cautious optimism among banking practitioners rather than uncritical enthusiasm. The high endorsement of AI's fraud-detection ability — 76.8% agreement (Figure 1) — echoes earlier work showing that AI-based systems can meaningfully reduce fraudulent activity and strengthen financial security (Truby et al., 2020). What is perhaps more interesting, though, is the gap between that figure and the somewhat lower agreement around minimizing financial loss (68.7%) and risk prediction (66.2%). One plausible reading is that practitioners distinguish between AI's diagnostic power — spotting that something is wrong — and its predictive or preventive power, which may still feel less mature or less proven in practice.

Privacy findings told a similarly nuanced story. Personal information security drew strong endorsement (72.1%; Figure 2), yet compliance with privacy regulation scored lowest among privacy items and carried the highest neutral response (66.7% positive, 20.3% neutral). This is broadly consistent with prior observations that regulatory compliance is often perceived as lagging behind the technical capability of AI systems, even where the underlying technology is otherwise trusted (Găbudeanu et al., 2021). It suggests that the harder, and perhaps more consequential, work of privacy governance may lie less in the technology itself and more in the surrounding legal and procedural apparatus (Miglionico, 2022).

Governance-related findings reinforce this interpretation. Fairness (42.4% agreement) and ethical decision-making (32.1% strong agreement) emerged as the most strongly endorsed governance qualities (Figure 3), which is worth sitting with for a moment: practitioners appear to weight the ethical texture of AI decision-making at least as heavily as its technical accuracy. This aligns with the broader RegTech literature, which frames regulatory and ethical alignment as central, not peripheral, to whether financial institutions can responsibly scale AI (Mohamed & Yildirim, 2021), and with work suggesting that explainability and ethical governance are primary drivers of client trust in AI-enabled financial services (Palomares et al., 2021).

The correlational results add a layer of statistical support to this narrative. Customer trust's strongest associations were with privacy protection (r = .756) and fraud detection (r = .721; Table 2), while privacy risk showed the strongest negative association with trust (r = -.603). Read together, these patterns are consistent with the idea that trust in AI-banking systems is not built primarily on convenience or efficiency, but on the perceived competence and integrity of the systems handling sensitive data — a conclusion that dovetails with earlier findings on trust formation in financial AI (Lui & Lamb, 2018). The clustering of algorithmic bias and data-misuse concern with privacy risk (r = .612 and r = .648, respectively) further hints that these "concern" constructs may function as a single underlying dimension of skepticism, worth exploring with confirmatory factor analysis in future work.

Finally, the multicollinearity diagnostics (Table 3) — tolerance values comfortably above 0.10 and VIF values well under 5 — indicate that the six study variables, while correlated in theoretically sensible ways, remain statistically distinct enough to support a regression model without redundancy concerns. This lends some confidence to the overall analytical structure, even as it leaves open questions this study could not fully answer: how these perceptions might differ across bank size, regulatory jurisdiction, or customer-facing versus back-office roles, and whether practitioner perceptions actually track customer perceptions, which were not measured here.

4.1 Limitations

Several limitations temper these conclusions. The purposive, non-random sampling strategy limits generalizability to the broader population of US banking professionals, let alone to customers themselves, whose perceptions were not directly assessed. All measures relied on self-reported perception rather than objective system performance data, and no common-method bias check was performed despite all constructs being drawn from the same self-report instrument. Reliability statistics for the three constructs (e.g., Cronbach's alpha) were not available for this analysis and should be reported in any final submission. Finally, the cross-sectional design captures a single point in time and cannot speak to how these perceptions might shift as AI systems, regulations, and public familiarity with them continue to evolve.

Figure 2. Respondent perceptions of AI-driven customer privacy protection and data security (N = 165). Bars represent the percentage of respondents endorsing each dimension — personal information security, data governance, customer data security, data breach prevention, data confidentiality, and regulatory compliance — on a five-point Likert scale, collapsed into positive, neutral, and negative response categories.

Figure 3. Respondent perceptions of responsible AI governance and customer trust (N = 165). Bars represent the percentage distribution of Agree and Strongly Agree responses across governance and trust dimensions — fairness, transparency, banking confidence, customer trust, ethical decision-making, and accountability — measured on a five-point Likert scale.

5. Conclusion

This study suggests that responsible AI governance is not a peripheral concern in banking but a fairly central one — closely bound up with how practitioners perceive fraud detection, privacy protection, and, ultimately, customer trust. Surveying 165 US banking, fintech, and IT professionals, we found broadly favorable perceptions of AI's fraud-detection and privacy-protection capabilities, alongside strong positive correlations linking both to customer trust, and a notable negative association between perceived privacy risk and trust. Multicollinearity diagnostics supported the statistical integrity of these relationships. While the findings are encouraging, they also point to where governance work remains unfinished — particularly around regulatory compliance and risk prediction. Future research extending beyond practitioner perception to customer-facing and longitudinal data would meaningfully strengthen these conclusions.

Acknowledgements

The authors M.N.H. et al., thank the banking, fintech, and information-technology professionals who volunteered their time to complete this survey.

Author Contributions

M.N.H.: conceptualization, methodology, data curation, formal analysis, writing - original draft, writing - review and editing. A.T.S.: conceptualization, investigation, writing - original draft, writing - review and editing. Both authors reviewed and approved the final manuscript.

Competing Financial Interests

The authors M.N.H. et al., declare no competing financial interests related to this work.

References