Hireforensics: An Intelligent Model for Analyzing Deceptive Patterns in Recruitment Ecosystems Using Machine Learning

Abstract Recruitment fraud has emerged as a serious global concern with the rapid growth of online job portals, social media hiring, and remote recruitment practices. Fraudulent recruiters exploit job seekers through fake job offers, phishing emails, counterfeit interviews, and financial scams, causing financial loss and psychological distress. Traditional rule-based and manual verification mechanisms are insufficient to detect evolving fraud patterns in real time. This paper presents HireForensics, an intelligent recruitment fraud detection system that leverages machine learning and natural language processing (NLP) techniques to identify deceptive patterns in recruitment ecosystems. The proposed system analyzes job descriptions, recruiter messages, communication behavior, and metadata to classify recruitment activities as legitimate or fraudulent. Multiple machine learning models including Logistic Regression, Random Forest, Support Vector Machines, and ensemble classifiers are implemented and evaluated. Experimental results demonstrate that the proposed model achieves high accuracy and robustness, making HireSafe AI a scalable, cost-effective, and reliable solution for safeguarding job seekers and recruitment platforms. Keywords Natural Language Processing (NLP), Random Forest, Ensemble Learning, Heuristic Analysis, TF-IDF, Logistic Regression