The rapid expansion of mobile financial services (MFSs) has brought about benefits in terms of financial inclusion in developing countries; however, threats have also emerged on the sides of cybersecurity and privacy. Traditional fraud-detection strategies are usually not responsive in time or adaptive to changing threat scenarios. This study investigates how artificial intelligence (AI) can be employed to strengthen fraud detection and methods to address user privacy concerns within MFS platforms in emerging markets. A mixed-method approach was adopted, i.e., a quantitative survey (n = 151) and a qualitative analysis of open-ended response. A reliability analysis showed internal consistency (Cronbach’s alpha > 0.70 across constructs). The descriptive results demonstrate that 95.4% of those questioned raised privacy concerns, whereas 78.2% recognized the benefits of AI-driven fraud detection. Regression analysis showed that AI significantly improved perceived security (β = 0.63, p < 0.01), although transparency and explainability were critical determinants of trust. The findings indicate that users consider AI a capable real-time fraud detection tool; however, doubts remain regarding data transparency, sharing with third parties, and lack of user-opted control, resulting in the erosion of user trust. The study also indicates that the socio-cultural factors and weak regulatory contexts weigh heavily on users’ acceptance of these AI-powered systems. This study proposes the promotion of Explainable AI (XAI) systems along with privacy-by-design user controls and localized communication approaches to foster trust and further adoption. The study contained within are thus a critical guide for policymakers, fintech developers, and providers, who seek to innovate with user protection within digital fintech.
Mollik et al. (2025) studied this question.