Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 7 Number 1 2026
RESEARCH ARTICLE (Open Access)
Artificial Intelligence and Financial Fraud Detection: Survey Evidence on Organizational Integrity and Risk Management from U.S. Professionals
Nasir Uddin 1*, Abdul Kadir 2, Md Yeasir Arafat 1
Journal of Primeasia 7 (1) 1-10 https://doi.org/10.25163/primeasia.7110906
Submitted: 31 August 2026 Revised: 01 December 2026 Accepted: 09 December 2026 Published: 11 December 2026
Abstract
Background: The rapid digitization of financial systems has made fraud schemes more complex and harder to detect using traditional, rule-based methods, pushing organizations toward artificial intelligence (AI) as a more adaptive alternative. Yet empirical evidence on how AI capability relates to organizational integrity and risk management — particularly within U.S. organizations — remains limited.
Methods: We conducted a cross-sectional survey of 145 professionals working in AI-exposed roles across U.S. financial, insurance, and technology organizations. Participants completed measures of AI Technology Readiness (AITR), Fraud Detection Capability (FDC), Organizational Integrity (OI), Risk Management Effectiveness (RME), and Financial Fraud Detection Performance (FFDP). Descriptive statistics, Pearson correlations, and multiple regression analysis were used to examine the hypothesized relationships.
Results: All constructs correlated positively with FFDP. The regression model explained 75.2% of the variance in FFDP (R² = .752, Adjusted R² = .745; F = 104.537, p < .001). FDC was the strongest predictor (β = .361), followed by AITR (β = .301), RME (β = .263), and OI (β = .227); all relationships were statistically significant (p < .001).
Conclusion: Financial fraud detection performance appears to depend not on AI capability alone, but on its integration with organizational readiness, governance, and risk management practice. These findings offer modest but grounded empirical support for treating AI-driven fraud detection as a broader organizational capability rather than a standalone technical fix.
Keywords: Artificial Intelligence; Financial Fraud Detection; Organizational Integrity; Risk Management Effectiveness; Fraud Prevention
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