IRO Journals
Journal of Information Technology and Digital World

Recallio AI: An Intelligent Multimodal Forensic Platform for Recruitment Fraud Detection and Prevention

Open Access
Published: 05 June, 2026
Pages: 155-166
  • Shanthi S. , Shanthi S.
    Assistant Professor, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    Assistant Professor, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Palani Surya S. , Palani Surya S.
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Petchiammal S. , Petchiammal S.
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Prasheetha R. , Prasheetha R.
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
  • Santhiya M. Santhiya M.
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
    UG Student, Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India
view PDF
view PDF

How to Cite

S., Shanthi, Palani Surya S., Petchiammal S., Prasheetha R., and Santhiya M. 2026. “Recallio AI: An Intelligent Multimodal Forensic Platform for Recruitment Fraud Detection and Prevention”. Journal of Information Technology and Digital World 8 (3): 155-66. https://doi.org/10.36548/jitdw.2026.3.002.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Recruitment Fraud
AI Forensics
Multimodal Analysis
Scam Detection
Gemini AI
OCR
Cybersecurity
Job Scam Prevention

Abstract

Recruitment fraud has become one of the major problems for cybersecurity owing to the exponential rise of online recruiting tools, social networking sites, and messaging services. Fraudsters use fake identities of recruiters, scams in job postings, phishing websites and links, and other techniques, leading to monetary loss and identity theft. In this paper, the Recallio AI tool, a multimodal forensic intelligent system for detecting recruitment fraud, is discussed. In this system, the use of Google Gemini AI, Optical Character Recognition (OCR), and web full-stack technologies in analyzing resumes, job description, recruiter chat, screenshots, physical posters, and information about the company is suggested. A combined approach based on scam detection, conversation forensic investigation, OCR-based street-side scanning, company verification, victim assistance, and analytical tools was implemented. The test data included 155 samples that were genuine and fake relating to recruitment processes. It was discovered that the framework under consideration delivered a 91.0% accurate detection rate. It becomes apparent that the framework can be effective when it comes to detecting cases of recruitment fraud both offline and online, promoting cybersecurity and forensic awareness.

References

  1. New FTC Data Show Skyrocketing Consumer Reports About Game-Like Online Job Scams. (2025, July 31). Federal Trade Commission. https://www.ftc.gov/news-events/news/press-releases/2024/12/new-ftc-data-show-skyrocketing-consumer-reports-about-game-online-job-scams.
  2. Roy, Pradeep Kumar, and Shivam Chahar. "Fake Profile Detection on Social Networking Websites: A Comprehensive Review." IEEE Transactions on Artificial Intelligence 1, no. 3 (2021): 271-285.
  3. Chang, Yuan-Chen, and Esma Aïmeur. "Chat or Trap? Detecting Scams in Messaging Applications with Large Language Models." In 2024 8th Cyber Security in Networking Conference (CSNet), IEEE, 2024, 92-99.
  4. Edwards, Graeme. Cybercrime Investigators Handbook. John Wiley & Sons, 2019.
  5. Fette, Ian, Norman Sadeh, and Anthony Tomasic. "Learning to Detect Phishing Emails." In Proceedings of the 16th international conference on World Wide Web, 2007, 649-656.
  6. Ihekweazu, Chukwuemeka, Elizabeth Adepeju Adelowo, and Naomi Aghado. "Digital Forensics in Action: A Case Study of Tracing Cybercriminals Behind Job Offer Spear Phishing Scams in Academic Institutions." In Proceedings of the ISCAP Conference ISSN, vol. 2473, 2024, 4901.
  7. Team, Gemini, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk et al. "Gemini: A Family of Highly Capable Multimodal Models." arXiv preprint arXiv:2312.11805 (2023).
  8. Anthropic, A. I. "Model Card and Evaluations for Claude Models." Anthropic Blog (2023).
  9. Smith, Ray. "An Overview of the Tesseract OCR Engine." In Ninth international conference on document analysis and recognition (ICDAR 2007), vol. 2, IEEE, 2007, 629-633.
  10. Sudharsan, S., and R. Sudha. "AI-Powered Fake Job Detection Using Multimodal Hybrid Intelligence." In 2026 4th International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), IEEE, 2026, 964-970.