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Leveraging proprietary job application data from a financial institution, we investigate the efficacy of machine learning (ML) in enhancing employee selection processes. We demonstrate that ML models can not only emulate human recruiters' decisions in matching applicants with employers but also substantially reduce inefficiencies in these matches. Critically, our most effective machine learning models excel in identifying job candidates likely to demonstrate a lack of commitment to the firm, as well as those poised to deliver exceptional job performance. Employing Shapley values, we illuminate the distinctions in decision-making between human recruiters and our ML models. In addition, our top-performing ML models exhibit reduced biases compared to human recruiters, against disadvantaged groups. These insights emphasize the transformative role of machine learning in optimizing employee selection and enhancing management control strategies.

zcarol2@ceibs.edu