Online Recruitment Detection And Prevention Techniques

Artificial inteligence and machine learning engineering

Online Recruitment Fraud (ORF) is a rising challenge in the digital recruitment industry, where malicious actors post fraudulent job advertisements to mislead applicants. These scams often mimic legitimate postings, making them difficult to detect with traditional rule-based approaches. Compounding the challenge is the issue of class imbalance, as fraudulent cases form only a small portion of datasets. This project introduces a comprehensive framework combining contextual language representations, data preprocessing, and imbalanced learning techniques such as SMOTE variants. Experimental evaluations demonstrate improved recall for fraudulent cases, supporting real-world use cases like analyst triage and automated screening. The system is designed to be accurate, robust, and explainable, with provisions for low-latency deployment and ongoing adaptability against adversarial attacks

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.