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A Machine Learning-Based AI Framework to Optimize the Recruitment Screening Process

Author

Listed:
  • Anshul Ujlayan

    (Delhi Technological University)

  • Sanjay Bhattacharya

    (Delhi Technological University)

  • Sonakshi

    (Gautam Buddha University)

Abstract

Organizations across industries face challenges in recruiting the right talent while expending precious resources and time. The complex process of fair screening and shortlisting could be substantially streamlined by deploying automated screening and matching of job applications. This study suggests an analytics-based approach for improving the competitiveness of the human resources recruitment process . Available tools have been used to extract the attributes from the profile job description and match them with prospective candidates' résumé from the database. Similarity analysis identified the most suitable applicant based on the desired attributes vs. resume. The results of the framework were trained on a random sample of 1029 job applicants' profiles of an IT company. It was able to reduce 80% of manual screening efforts. This is expected to directly reflect in a saving of man-hours and allied operating costs. Though the current study is limited to the context of an IT company in India, the proposed artificial intelligence-based framework holds immense potential to be extended across industries. The study contributes to both theory and practice by helping leaders, associations, policymakers, and academia, to strategize and optimize recruitment efforts.

Suggested Citation

  • Anshul Ujlayan & Sanjay Bhattacharya & Sonakshi, 2023. "A Machine Learning-Based AI Framework to Optimize the Recruitment Screening Process," International Journal of Global Business and Competitiveness, Springer, vol. 18(1), pages 38-53, December.
    • Handle: RePEc:spr:ijogbc:v:18:y:2023:i:1:d:10.1007_s42943-023-00086-y
    DOI: 10.1007/s42943-023-00086-y
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    References listed on IDEAS

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