Abstract
The rapid growth of online recruitment platforms has increased opportunities for job seekers but has also led to a significant rise in fraudulent job postings and recruitment scams. Fake job advertisements often deceive candidates by requesting sensitive personal information or demanding fraudulent payments. Detecting such scams manually is difficult and time-consuming for users. This research proposes a Real-Time AI Job Scam Detection System, an intelligent web-based platform designed to automatically analyze job postings and identify potential fraudulent recruitment offers. The system uses machine learning and natural language processing techniques to evaluate job descriptions and classify them as Scam or Genuine. The proposed system supports multiple input sources including text job descriptions, job posting URLs, email-based job offers, and browser extension-based job scanning from recruitment platforms such as Naukri, Indeed, and Internshala. The system extracts job content from these sources, validates whether the content represents a legitimate job requirement, and then performs scam detection using trained machine learning models. The system also provides scam probability percentage, risk score, suspicious indicators, and explanation of prediction results, enabling users to understand the reason behind the classification. Additionally, a Chrome browser extension allows real-time job analysis directly from job portals, making the solution highly practical for everyday users. The proposed system demonstrates how artificial intelligence can be applied to enhance online recruitment security and protect job seekers from fraudulent employment schemes.
Citations
IRE Journals:
Asan Nainar M, Surya Prakash K "Real-Time AI Job Scam Detection System" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2050-2058 https://doi.org/10.64388/IREV9I9-1715441
IEEE:
Asan Nainar M, Surya Prakash K
"Real-Time AI Job Scam Detection System" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715441
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