Vol. 1 No. 1 (2022): Emirati Journal of Business, Economics, & Social Studies
Articles

Identification Of Online Recruitment Fraud (ORF) Through Predictive Models

PDF
  • Riktesh Srivastava

Published 2022-04-12

Keywords

  • Artificial Neural Networks,
  • Logistic Regression,
  • Stochastic Gradient Descent,
  • CA,
  • Precision,
  • Recall,
  • Online Recruitment Fraud (ORF)
    • ...More
    Less

How to Cite

Identification Of Online Recruitment Fraud (ORF) Through Predictive Models. (2022). Emirati Journal of Business, Economics, & Social Studies, 1(1), 42-54. https://doi.org/10.54878/epv1hr57

Abstract

Job postings online have become popular these days due to connecting to job seekers around the world. There are also instances where the fraudulent employer posts a job online and expects people to apply to these postings. These fraudulent employers impend job seekers' privacy, spawns fake job offers, and wanes. We perceived that most of the Online Recruitment Fraud (ORF) has matching features. Though the user cannot categorize them, we propose using various predictive models like Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, Naïve Bayes, or Logistics Regression to detect them effortlessly. Dataset with 17780 job postings was downloaded from Kaggle to identify which proposed model best predicts the fraudulent job posting. The dataset includes 14 features to determine whether online job posting is fraudulent or non-fraudulent. 70% of these job postings train the model, and the remaining 30% test the model's efficiency. The outcomes of each model are predicted using four evaluation metrics – Classification Accuracy (CA), Precision, Recall and F-1 score. The research found its suitability from two sides: the websites can identify fake jobs before being published, and job seekers are sheltered from fraudulent job postings.

PDF

References

  1. Agency Central. (2019). Spotting fake job adverts: What jobseekers need to know. https://www.agencycentral.co.uk/art icles/2017-06/what-jobseekers-needto-know-about-fake-job-ads.htm
  2. Alharby, F., & Alghamdi, B. (2019). An Intelligent Model for Online Recruitment Fraud Detection. Journal of Information Security, 10(3), 720–726. https://doi.org/10.4236/jis.2019.103009
  3. Cao, S., Yang, X., Chen, C., Zhou, J., Li, X., & Qi, Y. (2019). TitAnt: Online Realtime Transaction Fraud Detection in Ant Financial. ArXiv:1906.07407 [Cs, Stat]. http://arxiv.org/abs/1906.07407
  4. Choi, J. K., Hyerin Won, Minsun Shim, Seungah Hong, Eunjung. (2020). Fake Job Recruitment Detection UsingMachine Learning Approach. International Journal of Engineering Trends and Technology - IJETT. https://www.ijettjournal.org/archive/ ijett-v68i4p209s
  5. CNN, A. E. (2021). Looking for a job? Make sure it's real first. CNN. https://www.cnn.com/2021/04/24/bu siness/employment-scams-2020- trnd/index.html
  6. DNA. (2021). Fake job portals cheated 27,000 employed people, collected Rs 1.09 crore in a month. DNA India. https://www.dnaindia.com/delhi/rep ort-fake-job-portals-cheated-27000- employed-people-collected-rs-109- crore-in-a-month-2854770
  7. Kim, J., Kim, H.-J., & Kim, H. (2019). Fraud detection for job placement using hierarchical clusters-based deep neural networks. Applied Intelligence, 49(8), 2842–2861. https://doi.org/10.1007/s10489-019- 01419-2
  8. Lal, S., Jiaswal, R., Sardana, N., Verma, A., Kaur, A., & Mourya, R. (2019). ORFDetector: Ensemble Learning Based Online Recruitment Fraud Detection. 2019 Twelfth International Conference on Contemporary Computing (IC3), 1–5. https://doi.org/10.1109/IC3.2019.884 4879
  9. Mehta, R. (2020). Fake job offers: How to avoid getting duped in job scams. Economics Times. https://economictimes.indiatimes.co m/wealth/earn/fake-job-offers-howto-avoid-getting-duped-in-jobscams/articleshow/69173183.cms?fro m=mdr
  10. Nasser, I. M., & Alzaanin, A. H. (2020). Machine Learning and Job Posting Classification: A Comparative Study. 9, 9.
  11. Phillips, P. (2021). Council Post: Are Online Scammers Committing Recruitment Fraud In Your Company's Name? Forbes. https://www.forbes.com/sites/forbes humanresourcescouncil/2021/04/14/ are-online-scammers-committingrecruitment-fraud-in-yourcompanys-name/
  12. Porter, K. (2021). Don't fall for online employment and job scams. https://us.norton.com/internetsecuri ty-online-scams-avoid-jobscams.html
  13. Ranparia, D., Kumari, S., & Sahani, A. (2020). Fake Job Prediction using Sequential Network. 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 339–343. https://doi.org/10.1109/ICIIS51140.20 20.9342738
  14. Ryan, L. (2018). The Job Search Scam 90% Of Candidates Fall For. Forbes. https://www.forbes.com/sites/lizryan /2018/05/06/the-job-search-scam-90- of-candidates-fall-for/
  15. Shibly, F., Sharma, U., & Naleer, H. (2021). Performance Comparison of Two Class Boosted Decision Tree snd Two Class Decision Forest Algorithms in Predicting Fake Job Postings. Annals of the Romanian Society for Cell Biology, 2462-2472- 2462–2472. https://www.annalsofrscb.ro/index.p hp/journal/article/view/2778
  16. Shishupal, Prof. R. S., . V., Mane, S., Singh, V., & Wasekar, D. (2021). Virtual Assistant for Prediction of Fake Job Profile Using Machine Learning. International Journal of Advanced Research in Science, Communication and Technology, 171– 175. https://doi.org/10.48175/IJARSCT-885
  17. Shree, R. A., Nirmala, D., Sweatha, S., & Sneha, S. (2021). Ensemble Modeling on Job Scam Detection. Journal of Physics: Conference Series, 1916(1), 012167. https://doi.org/10.1088/1742- 6596/1916/1/012167
  18. Vidros, S., Kolias, C., Kambourakis, G., & Akoglu, L. (2017). Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset. Future Internet, 9(1), 6. https://doi.org/10.3390/fi9010006

Similar Articles

You may also start an advanced similarity search for this article.