A Comprehensive AI-Powered System for Screening Resumes and Ranking Job Applicants for Optimal Hiring Decisions

Abstract

The rapid expansion of digital recruitment platforms has significantly increased the number of resumes submitted for job opportunities, creating a major challenge for recruiters to efficiently identify suitable candidates. Traditional resume screening methods are often time-consuming, subjective, and susceptible to human bias, resulting in inconsistent hiring decisions. To overcome these limitations, this study proposes an AI-powered resume screening and candidate ranking system that utilizes Natural Language Processing (NLP), machine learning techniques, and automated ranking strategies. The system extracts essential information such as skills, work experience, education, and certifications from resumes and matches them with job requirements to generate a suitability score. Advanced classification and ranking algorithms are employed to prioritize candidates effectively. The proposed framework integrates data preprocessing, feature extraction, and supervised learning techniques to improve accuracy, fairness, and efficiency. Unlike traditional keyword-based approaches, the system focuses on contextual understanding of resume content. Experimental results indicate high classification and ranking accuracy, demonstrating its effectiveness in real-world recruitment environments. Overall, the system enhances hiring efficiency, minimizes bias, and supports data-driven decision-making in human resource management.