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Blockchain and Machine Learning in Talent Acquisition: A Review on Credential Verification and Recruitment Optimization

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  • S M Asiful Islam SAKY

  • Farhana AKTER

  • Rabiul ISLAM

  • Yeasmin AKTER

  • Yeasmin AKTER

Abstract

The rapid evolution of recruitment processes has prompted organizations to explore advanced technologies that enhance efficiency, transparency, and fairness in talent acquisition. This review critically examines the integration of Blockchain and Machine Learning (ML) as transformative tools in addressing long-standing challenges such as credential fraud, skills mismatch, unconscious bias, and lack of process transparency. Blockchain provides a decentralized, immutable ledger for secure storage and verification of academic and professional credentials, thereby reducing fraudulent claims and streamlining verification through smart contracts. The convergence of these technologies creates robust frameworks where ML algorithms operate on authenticated data supplied by blockchain systems, reinforcing trust, accountability, and efficiency in recruitment workflows. This study highlights both the transformative opportunities and practical challenges of these technologies, providing insights for academia, practitioners, and policymakers to design sustainable recruitment strategies aligned with modern workforce demands.

Suggested Citation

  • S M Asiful Islam SAKY & Farhana AKTER & Rabiul ISLAM & Yeasmin AKTER & Yeasmin AKTER, 2026. "Blockchain and Machine Learning in Talent Acquisition: A Review on Credential Verification and Recruitment Optimization," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 30(1), pages 54-70.
    • Handle: RePEc:aes:infoec:v:30:y:2026:i:1:p:54-70
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