Online Recruitment Fraud (ORF) Detection System

Abstract

The rise of digital platforms for job recruitment has brought with it a growing threat of fraudulent job postings, undermining the trust and safety of online hiring systems. This paper proposes an advanced fraud detection system based on the ALBERT (A Lite BERT) model to identify fraudulent job postings. The system will utilize a dataset created by merging job postings from multiple sources to better capture both legitimate and fraudulent job listings. A comprehensive pre-processing pipeline, including data cleaning and feature engineering, will be applied to prepare the data for model training. The system will incorporate various techniques, such as SMOTE (Synthetic Minority Over sampling Technique), to address class imbalance. The ALBERT model will then be fine-tuned to classify job postings, and the system will be evaluated using standard performance metrics like accuracy, precision, recall, and F1-score. The goal is to provide an effective and scalable solution for detecting fraudulent job postings in real-time, enhancing the overall security and trustworthiness of online recruitment platforms. The accuracy of the proposed technique is 99.82% which is higher than other well-known existing techniques.