DEEP LEARNING SOLUTIONS FORDETECTING FRAUD IN ONLINERECRUITMENT

Most companies nowadays are using digital platforms for the recruitment of newemployees to make the hiring process easier. The rapid increase in the use of online platforms forjob posting has resulted in fraudulent advertising. The scammers are making money throughfraudulent job postings. Online recruitment fraud has emerged as an important issue in cybercrime.Therefore, it is necessary to detect fake job postings to get rid of online job scams. In recent studies,traditional machine learning and deep learning algorithms have been implemented to detect fake jobpostings; this research aims to use two transformer-based deep learning models, i.e., BidirectionalEncoder Representations from Transformers and Robustly Optimized BERT-Pretraining Approach(RoBERTa) to detect fake job postings precisely. In this research, a novel dataset of fake jobpostings is proposed, formed by the combination of job postings from three different sources.Existing benchmark datasets are outdated and limited due to knowledge of specific job postings,which limits the existing models’ capability in detecting fraudulent jobs. Hence, we extend it withthe latest job postings. Exploratory Data Analysis (EDA) highlights the class imbalance problem indetecting fake jobs, which tends the model to act aggressively toward the minority class.Responding to overcome this problem, the work at hand implements ten top-performing SyntheticMinority Oversampling Technique (SMOTE) variants. The models’ performances balanced by each SMOTE variant are analyzed and compared. Allimplemented approaches are performed competitively. However, BERT+SMOBD SMOTEachieved the highest balanced accuracy and recall of about 90%.