This study examines how European small and medium-sized enterprises (SMEs) adopt and integrate artificial intelligence (AI) technologies to improve fraud detection and risk mitigation. While AI has been widely studied in large organizations, evidence on its application within SMEs remains limited,
particularly in the context of financial fraud. Little is known about which AI tools SMEs actually deploy, how these tools affect detection effectiveness, and which organizational and regulatory barriers influence successful adoption. This study seeks to fill this gap by providing empirical evidence on SME adoption of AI in fraud detection. The study employs a qualitative, exploratory design based on semi-structured interviews with Dutch SMEs from various sectors. Data was analyzed through inductive thematic analysis, allowing patterns and themes to emerge from the participants’ experiences and perspectives. The methodology enabled in-depth exploration of adoption drivers, barriers, and perceptions, while also acknowledging the limitations of a small, single-country sample. The findings indicate that AI adoption, applied to improve fraud detection and risk mitigation, among SMEs is still at an early stage. What is described as AI often turns out to be modest digitalization tools or lightweight automations, such as invoice scanners, time-tracking apps, no-/low-code workflows, or generative assistants, that improve efficiency and process discipline, but do not perform autonomous fraud detection. These tools remain supplemental and supporting rather than replacing human judgment. The main barriers of adoption come down to bad data quality, limited technical knowledge, high costs, and uncertainty about regulations. Still, value can be created when companies take a balanced approach: keeping people in the loop, starting small, and putting effort into getting their data in order. This study adds new real‑world insights into how SMEs are actually experimenting with AI for fraud detection. The study concludes that while full-scale integration remains rare, SMEs can benefit by focusing on practical gains rather than radical change. The key recommendation is that investing in data infrastructure today is the most critical step SMEs can take to prepare for AI’s expanding role in fraud detection and risk management.