AI Adoption and Recruitment Efficiency in European Banking: A Mixed-Method Analysis

The adoption of Artificial Intelligence (AI) is reshaping recruitment processes in the European banking sector, where efficiency, accuracy, and compliance are strategic imperatives. This study investigates the extent to which AI improves recruitment efficiency, candidate selection quality, organisational outcomes, and candidate trust. Using a mixed-method approach, data were collected from 200 HR professionals and managers in European banks and supplemented with secondary industry evidence. Descriptive statistics, correlation, and regression analyses confirm that AI-driven recruitment significantly reduces time-to-hire and improves candidate-job matching, with recruitment process efficiency (β = 0.562, p < 0.001) and structured evaluation criteria (β = 0.377, p = 0.002) emerging as the strongest predictors of positive organisational outcomes. However, results also indicate that excessive reliance on automation can negatively affect candidate trust (β = −0.259, p < 0.05). These findings extend theoretical debates by applying the Technology Acceptance Model, the Resource-Based View, and Human Capital Theory to the context of banking recruitment, highlighting AI as both a strategic resource and a source of ethical and transparency challenges. Practical implications include the need for hybrid recruitment models combining automation with human oversight, enhanced transparency in candidate communication, and strict alignment with the EU AI Act. This study contributes original empirical evidence from European banking, offering theoretical, managerial, and policy insights into the responsible and effective adoption of AI in recruitment.