AI-powered resume screening for candidates

ABSTRACT

Recruitment is a critical function for organizations, particularly in fast-paced industries where acquiring the right talent significantly impacts productivity and growth. Traditional resume screening methods are often time-consuming, prone to bias, and inconsistent in evaluating candidate suitability. This study proposes an AI-enabled Candidate Resume Screening System aimed at improving the efficiency and accuracy of the hiring process. The system leverages historical recruitment data, job descriptions, and unstructured resume content to provide intelligent and automated candidate shortlisting. By applying both supervised and unsupervised machine learning algorithms—such as Logistic Regression, Random Forest, and transformer-based NLP models—the system identifies the most suitable candidates for a given role and ranks them based on skill relevance, experience, and educational background. The application is deployed through a user-friendly web interface, making it accessible to HR professionals regardless of technical expertise. By integrating data-driven decision-making into the recruitment workflow, the system enhances hiring efficiency, reduces subjectivity, and contributes to more fair and effective talent acquisition.

Key Words: Resume Screening, Artificial Intelligence, Machine Learning, Natural Language Processing, Recruitment Automation.