AI-Powered Fake Resume Analyser and Candidate Credibility Detection System Using Machine Learning

Author

Hemalatha M A, Dharshini G, Archana Devi M, Deeksha P, Dharani R S


Abstract

 Recruiters routinely receive hundreds of digital resumes for a single vacancy, and verifying every claim manually is slow, inconsistent and prone to oversight. Fabricated qualifications, overlapping employment periods, inflated experience and vague or templated descriptions can therefore pass the first screening stage unnoticed. This paper presents the design of an AI-powered, web-based Fake Resume Analyser and Candidate Credibility Detection System that supports recruiters in finding potentially misleading or inconsistent information in candidate resumes. The system accepts resumes in PDF and DOCX formats, extracts and preprocesses the text, and applies Natural Language Processing (NLP), rule-based consistency checks and Machine Learning techniques, with the Scikit-learn library as the modelling framework, to detect timeline conflicts, suspicious employment and education details and other anomalies. The findings are combined into an explainable candidate risk score that is presented on a recruiter dashboard together with the specific indicators that raised it, and a job-description matcher and an audit history support everyday screening work. The system is intentionally a decision-support tool: it flags items for human verification and never decides that a candidate is fraudulent. The expected benefits are reduced screening effort, structured and reproducible analysis and a clearer starting point for verification. The limitations include dependence on document quality, the possibility of bias in trained models and the fact that automated analysis cannot prove fraud. Quantitative evaluation results are not reported in this manuscript and are left as clearly marked placeholders to be completed after testing. Future work includes transformer-based language models, larger verified datasets, improved explainability, multi-language support and secure cloud deployment.


Keywords

Artificial Intelligence, Fake Resume Detection, Machine Learning, Natural Language Processing, Resume Analysis, Candidate Credibility, Anomaly Detection, Recruitment Automation.



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References


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