In today’s competitive business environment, digital transformation is crucial for organizational success. The Jordanian banking sector faces the challenges of adapting to rapid digital advancements, evolving customer expectations, and intense competition. This study investigated the impact of digital transformation capabilities—technological adaptation, strategic positioning, and competitive positioning—on perceived performance among 129 bank managers from 16 Jordanian commercial banks. Data were collected via a web-based survey that included a 29-item perceptual scale using a 5-point Likert scale. Multiple linear regression analysis revealed a significant positive relationship between these capabilities and perceived performance, explaining 68% of the variance. Specifically, technological adaptation (β = 0.310), strategic positioning (β = 0.260), and competitive positioning (β = 0.360) all significantly predicted perceived performance. Harman’s single-factor test indicated minimal common method bias, and strong positive correlations were found among all study variables. This research underscores the importance of a holistic digital transformation strategy for Jordanian banks, emphasizing the need for strategic investments in technology, competitive differentiation, and alignment with business objectives. Future research should explore additional factors such as organizational culture and regulatory frameworks and incorporate objective performance measures to provide a more comprehensive understanding of the impact of digital transformation. This study offers valuable insights for practitioners, policymakers, and researchers seeking to navigate digital disruption and foster business growth.

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