Journal of risk and financial management, Journal Year: 2025, Volume and Issue: 18(4), P. 217 - 217
Published: April 18, 2025
This paper examines the uptake of AI-driven fraud detection systems among financial institutions in UAE and Qatar, with a special focus on trust, transparency, perceptions fairness. Despite promise AI operations identifying anomalies, unclear decision-making processes algorithmic bias constrain its extensive acceptance, especially regulation-driven banking sectors. study uses quantitative strategy based Partial Least Squares Structural Equation Modeling (PLS-SEM) Multi-Group Analysis (MGA) survey responses from 409 bank professionals, such as auditors compliance officers. shows that transparency greatly enhances which is leading predictor uptake. Fairness perception mediates negative impacts bias, emphasizing important role establishing system credibility. The analysis subgroups differential regional professional variations trust fairness sensitivity, where internal highly AI-exposed subjects are found to exhibit higher adoption preparedness. Compliance regulations also emerges positive enabler adoption. concludes suggestions for practical implementation by banks, developers, regulators align deployment ethical regulatory aspirations. It recommends transparent, explainable, fairness-sensitive tools essential promoting findings provide guide responsible, trust-driven detection.
Language: Английский