Naïve Bayes classifier for optimizing personnel selection process in financial industry

Algorithmic human resources management (HRM) is becoming increasingly popular among organizations and many HRM processes include automated decision making functions. Research very active in the domain, spans across machine learning data mining, aiming to provide accurate methods predict best candidates for job roles, or personnel development others. In this work, we present a Naïve Bayes based model, which focuses on preliminary application screening steps, suggest suitable applicants further processing, number of features. The model presented, along with an real case worked financial organization using primary selected from candidate applications. results are promising demonstrate that mix professional expertise algorithmic support may optimize HMR processes.