Unmasking Employment Scam: An Automated System to Detect Fraudulent Job Postings
N Murali Krishna, Guthikonda Karthik, G Pranay, K Balaji
An increased fraudulent job posting continues to pose a grave threat to the job seeker who is more or
less susceptible to financial and personal risks. The paper takes this issue to solve it with the help of
Employment Scam Aegean Dataset (EMSCAD) using the powerful machine learning algorithm called
XGBoost, for classification of jobs posted as real or fake ones, which, to a good approximation, will
show scams around near 98% with robust computational efficiency. For this reason, it will certainly be
of service in high volumes. With the implementation of the solution in a website for jobs, the purpose of
the solution will attempt to improve security for job markets and prevent victims from being misguided
while applying.
