Online Recruitment Fraud Detection Using Machine Learning

Abstract - The research suggests using machine learning classification methods to identify and prevent fake job postings online. Nowadays, many companies prefer to advertise their job vacancies on the internet for easy access by job seekers. However, there are scammers who trick people into paying for non-existent jobs. Through data analysis and machine learning, we can distinguish between legitimate and fake job postings. Various algorithms are utilized to detect fraudulent posts and protect job seekers from falling victim to scams. The system is designed to teach the model how to distinguish between real and fake job listings using past data on fraudulent and legitimate job postings. Initially, supervised learning algorithms like classification techniques can be used to tackle the problem of identifying scammers in job ads. It will utilize multiple machine learning algorithms and choose the one with the best accuracy in predicting if a job posting is legitimate or not. Key Words: Fraud Job, Job Seeker, Machine Learning, Internet Recruitment, Classification

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-06-16
DOI
https://doi.org/10.55041/ijsrem50445
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Online Recruitment Fraud Detection Using Machine Learning

Delip Rao
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Imbalanced Data Classification Techniques
article

Online Recruitment Fraud Detection Using Machine Learning

Delip Rao
article en

Abstract

Abstract - The research suggests using machine learning classification methods to identify and prevent fake job postings online. Nowadays, many companies prefer to advertise their job vacancies on the internet for easy access by job seekers. However, there are scammers who trick people into paying for non-existent jobs. Through data analysis and machine learning, we can distinguish between legitimate and fake job postings. Various algorithms are utilized to detect fraudulent posts and protect job seekers from falling victim to scams. The system is designed to teach the model how to distinguish between real and fake job listings using past data on fraudulent and legitimate job postings. Initially, supervised learning algorithms like classification techniques can be used to tackle the problem of identifying scammers in job ads. It will utilize multiple machine learning algorithms and choose the one with the best accuracy in predicting if a job posting is legitimate or not. Key Words: Fraud Job, Job Seeker, Machine Learning, Internet Recruitment, Classification

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(06)
Openalex Percentile: Top 93%
Imbalanced Data Classification Techniques
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