EFFICIENT ONLINE HIRING SCAM DETECTION USING AIPOWERED DEEP LEARNING MODELS

Abstract

The rapid growth of online recruitment platforms and digital hiring services has significantly increased the risk of recruitment fraud and job scams. Fraudulent job postings, fake employer profiles, and phishing-based recruitment activities have caused financial loss, identity theft, and security threats for job seekers worldwide. Traditional fraud detection techniques often fail to identify sophisticated and dynamically changing scam patterns, creating the need for intelligent and automated detection systems. This paper presents an efficient online hiring scam detection framework using AI-powered deep learning models to identify fraudulent recruitment activities with high accuracy and reliability. The proposed system utilizes advanced natural language processing and deep learning techniques to analyze job descriptions, employer information, communication patterns, and recruitment behavior. Multiple deep learning architectures, including convolutional neural networks and recurrent neural networks, are integrated to extract semantic and contextual features from recruitment data. Data preprocessing and feature engineering techniques are employed to improve classification performance and reduce false positives. The framework is trained using labeled datasets containing legitimate and fraudulent job postings to enhance model generalization and robustness. Experimental results demonstrate that the proposed AI-powered framework achieves superior detection accuracy, faster processing speed, and improved scalability compared to traditional machine learning approaches. The system effectively identifies fake job advertisements, phishing attempts, and suspicious recruitment activities in real time. Overall, the proposed model provides a reliable, scalable, and intelligent solution for enhancing trust and security in online recruitment platforms.