AI adoption linked to perceived fraud detection in Jordanian Islamic banks

Employees’ confidence in their financial judgment appears to help explain the association between artificial intelligence adoption and stronger perceptions of money-laundering and fraud-detection effectiveness in Jordanian Islamic banks. In a cross-sectional survey of 115 bank employees, AI adoption had a significant positive association with perceived detection effectiveness after financial self-efficacy was included in the model. The researchers characterized the overall pattern as complementary partial mediation, meaning that financial self-efficacy accounted for part, but not all, of the relationship between AI adoption and perceived effectiveness.

The participants worked in compliance, Shariah compliance, Shariah supervision, internal audit, auditing and risk-management functions. The questionnaire contained 35 items measuring AI adoption, financial self-efficacy, perceived effectiveness in detecting money laundering and financial fraud, and demographic characteristics. Reliability and validity tests, correlation analysis, standardized multiple regression and regression-based mediation analysis were used to examine the relationships.

The direct association between AI adoption and perceived detection effectiveness was positive, with a standardized coefficient of β = 0.296 and p = 0.001. AI adoption was also strongly associated with financial self-efficacy at β = 0.640, p < 0.001, while financial self-efficacy was positively associated with perceived detection effectiveness at β = 0.491, p < 0.001. The mediation model estimated an indirect effect of AI adoption on perceived detection effectiveness through financial self-efficacy of 0.314.

The reported Sobel test result was z = 4.806, p < 0.001, while the variance accounted for by the mediator, or VAF, was 51.46%. These results are consistent with the authors’ interpretation that employees’ confidence in interpreting and using financial information may form a meaningful statistical pathway connecting AI adoption with perceptions of anti-money-laundering and fraud-detection performance. They do not show that AI reduced money laundering, financial fraud or regulatory violations.

The outcome assessed in the study was employees’ perceived detection effectiveness rather than independently verified rates of crime detection. The authors recommend combining AI-supported monitoring systems with structured training, human-in-the-loop governance and Shariah-sensitive regulatory procedures in Islamic banks. Their proposed approach treats technology as part of a wider institutional process in which employees interpret and act on system outputs.

Several features of the study limit stronger conclusions. Its cross-sectional design cannot establish which factor influences which, and the sample comprised 115 employees in one national banking context, so the findings may not transfer to other institutions, occupational groups or countries. The evidence therefore supports an association among technology use, financial self-efficacy and perceived detection performance, rather than a demonstrated causal improvement in real-world financial-crime detection.

Subject of Research: Financial self-efficacy and perceived financial-crime detection among employees of Jordanian Islamic banks

Article Title: Financial self efficacy as a mediator of artificial intelligence use in detecting money laundering and financial fraud in Jordanian Islamic banks

Article References: Ghaith, M. A., Dannoun, Z. O., Alzyadat, E. A. H., & Al-Hakim, M. S. (2026). Financial self efficacy as a mediator of artificial intelligence use in detecting money laundering and financial fraud in Jordanian Islamic banks. Discover Sustainability. https://doi.org/10.1007/s43621-026-04775-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04775-5

Keywords: artificial intelligence, financial self-efficacy, money laundering, financial fraud, Islamic banks, Jordan, mediation analysis, compliance technology, Financial, self, efficacy, mediator