RESUMETRUST: A FULL-STACK TALENT PLATFORM WITH COMPOSITE TRUST SCORE FOR VERIFIED HIRING

Abstract

The rapid growth of digital recruitment has led to an overwhelming volume of semistructured resume data, creating challenges in accurately evaluating candidate credibility and increasing the risk of resume fraud. This project, “ResumeTrust: A FullStack Talent Platform with Composite Trust Score for Verified Hiring,” presents an innovative solution to transform traditional resumes into verified, data-driven professional profiles. The proposed system introduces a ThreePillar Verification Architecture consisting of automated resume parsing using Natural Language Processing (NLP), MCQ-based skill assessment for competency validation, and regex-based certification verification to ensure authenticity. These components work together to generate a composite Trust Score (0–100), which quantifies candidate credibility by integrating education, verified certifications, and validated skills. The platform is developed as a full-stack web application using Flask, MongoDB Atlas, and modern frontend technologies, providing separate dashboards for students and recruiters. It also incorporates advanced visual analytics such as career trajectory charts and skill radar graphs, enabling recruiters to make informed, objective hiring decisions. Performance evaluation demonstrates efficient processing, with resume parsing completed in under two seconds.