Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches

Abstract

Online recruitment platforms have become popular channels for job seekers and employers. However, the growth of these platforms has also increased the risk of Online Recruitment Fraud (ORF), where fraudsters post fake job advertisements to steal personal information, financial details, or conduct phishing attacks. Traditional rule-based and machine learning methods struggle to detect sophisticated fraudulent job postings. This research proposes a deep learning-based ORF detection system that analyzes job descriptions, recruiter information, and behavioral patterns to identify fraudulent postings. Natural Language Processing (NLP) and deep neural networks are used to extract contextual patterns from job advertisements. The proposed system improves fraud detection accuracy by learning complex textual and behavioral features. The model assists recruitment platforms in automatically identifying suspicious job posts, thereby protecting job seekers and maintaining platform credibility.