Investigation of Deep Learning Approaches for Online Recruitment Fraud Detection

G Mabuchan; B Rizwana; K Vinod Kumar; A Jasmitha; P Prasanthi

doi:10.5281/zenodo.15163735

Authors

G Mabuchan Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
B Rizwana Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Vinod Kumar Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
A Jasmitha Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Prasanthi Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India

DOI:

https://doi.org/10.5281/zenodo.15163735

Keywords:

Class imbalance, data augmentation, deep learning, employment scam, fraud detection, online recruitment, SMOTE

Abstract

Modern companies increasingly rely on digital platforms to recruit new employees and streamline the hiring process. However, the surge in online job postings has led to a rise in fraudulent advertisements, where scammers exploit job seekers for financial gain. Online recruitment fraud has become a significant concern in cybercrime, necessitating effective detection mechanisms to combat fake job listings. While traditional machine learning and deep learning models have been employed for this task, this research explores the effectiveness of two transformer-based deep learning models—Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa) in accurately identifying fraudulent job postings.

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Investigation of Deep Learning Approaches for Online Recruitment Fraud Detection

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